Non-invasive methods for heart rate measurement in fish based on photoplethysmography.

Heart rate is a crucial physiological indicator for fish, but current measurement methods are often invasive or require delicate manipulation. In this study, we introduced two non-invasive and easy-to-operate methods based on photoplethysmography, namely reflectance-type photoplethysmography (PPG) and remote photoplethysmography (rPPG), which we applied to the large yellow croaker (Larimichthys crocea). PPG showed perfect synchronization with electrocardiogram (ECG), with a Pearson's correlation coefficient of 0.99999. For rPPG, the results showed good agreement with ECG. Under active provision of green light, the Pearson's correlation coefficient was 0.966, surpassing the value of 0.947 under natural light. Additionally, the root mean square error was 0.810, which was lower than the value of 1.30 under natural light, indicating not only that the rPPG method had relatively high accuracy but also that green light may have the potential to further improve its accuracy.

Genital arousal and responsive desire among women with and without sexual interest/arousal disorder symptoms.

BACKGROUND: Models depicting sexual desire as responsive to sexual arousal may be particularly apt for women experiencing arousal or desire difficulties, and the degree to which arousal triggers desire may depend on the relationship context and desire target and timing-yet, these associations have not been directly tested among women with and without sexual interest/arousal disorder (SIAD). AIM: To assess the role of SIAD status and relationship satisfaction in the associations between genital arousal and 4 types of responsive desire. METHODS: One hundred women (n = 27 meeting diagnostic criteria for SIAD) in romantic relationships with men viewed a sexual film (pleasurable intimate depiction of oral sex and penile-vaginal intercourse) while their genital arousal was recorded via vaginal photoplethysmography (n = 63) or thermal imaging of the labia (n = 37). Partner and solitary desire was assessed immediately before and after the film (immediate desire) and 3 days later (delayed desire). OUTCOMES: Outcomes consisted of genital response (z scored by method) and associations between genital response and responsive sexual desire. RESULTS: The key difference between women with and without SIAD was not in their ability to experience genital arousal but in how their genital responses translated to responsive sexual desire. Women with SIAD actually exhibited greater genital arousal than unaffected women. Associations between genital arousal and desire were significant only for women with SIAD and depended on relationship satisfaction and desire type. For women with SIAD with low relationship satisfaction, higher arousal predicted lower immediate desire for a partner; for those with high relationship satisfaction, arousal was either positively related (vaginal photoplethysmography) or unrelated (thermal imaging of the labia) to immediate desire for a partner. Associations with other desire types were not significant. CLINICAL IMPLICATIONS: Patterns of genital arousal and partner-specific responsive desire among women affected with SIAD were indicative of an avoidance model in response to heightened genital arousal, unless relationship satisfaction was high; attending to genital arousal sensations could be a means of triggering sexual desire for women with SIAD who are satisfied in their relationships. STRENGTHS AND LIMITATIONS: This is one of the first sexual psychophysiologic studies to connect relationship factors to patterns of sexual response. The differing arousal assessment procedures and lack of official diagnosis may have attenuated results. The homogeneous sample and in-person session requirement limit generalizability. CONCLUSION: When compared with unaffected women, women affected by SIAD may exhibit stronger arousal responses with sufficiently incentivized sexual stimuli, and the connection between their genital arousal and responsive desire for their partners may be stronger and more dependent on relationship context.

Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation.

AIMS: Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions. METHODS AND RESULTS: Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7-99.9%], specificity (99.9%; CI: 99.8-100.0%), positive predictive value (99.6%; CI: 99.1-100.0%), and negative predictive value (99.6%; CI: 99.0-100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland-Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m. CONCLUSION: Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

Validation of a novel smartphone-based photoplethysmographic method for ambulatory heart rhythm diagnostics: the SMARTBEATS study.

AIMS: In the current guidelines, smartphone photoplethysmography (PPG) is not recommended for diagnosis of atrial fibrillation (AF), without a confirmatory electrocardiogram (ECG) recording. Previous validation studies have been performed under supervision in healthcare settings, with limited generalizability of the results. We aim to investigate the diagnostic performance of a smartphone-PPG method in a real-world setting, with ambulatory unsupervised smartphone-PPG recordings, compared with simultaneous ECG recordings and including patients with atrial flutter (AFL). METHODS AND RESULTS: Unselected patients undergoing direct current cardioversion for treatment of AF or AFL were asked to perform 1-min heart rhythm recordings post-treatment, at least twice daily for 30 days at home, using an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile). Photoplethysmography and ECG recordings were read independently by two experienced readers. In total, 280 patients recorded 18 005 simultaneous PPG and ECG recordings. Sufficient quality for diagnosis was seen in 96.9% (PPG) vs. 95.1% (ECG) of the recordings (P < 0.001). Manual reading of the PPG recordings, compared with manually interpreted ECG recordings, had a sensitivity, specificity, and overall accuracy of 97.7%, 99.4%, and 98.9% with AFL recordings included and 99.0%, 99.7%, and 99.5%, respectively, with AFL recordings excluded. CONCLUSION: A novel smartphone-PPG method can be used by patients unsupervised at home to achieve accurate heart rhythm diagnostics of AF and AFL with very high sensitivity and specificity. This smartphone-PPG device can be used as an independent heart rhythm diagnostic device following cardioversion, without the requirement of confirmation with ECG.

Smartphone app-based approximation of time spent with atrial fibrillation and symptoms in patients after catheter ablation: data from the TeleCheck-AF project.

AIMS: Reduction of atrial fibrillation (AF) burden is the preferred outcome measure over categorical AF rhythm recurrence after AF ablation. In this sub-analysis of the TeleCheck-AF project, we tested the feasibility of smartphone app-based approximation of time spent with AF and/or symptoms. METHODS AND RESULTS: Patients scheduled for at least one teleconsultation during the 12-month follow-up after AF ablation were instructed to use a smartphone photoplethysmography-based application for simultaneous symptom and rhythm monitoring three times daily for 1 week. Proxies of time spent with AF and/or symptoms (% recordings, load, and % days), temporal aggregation of AF and/or symptoms (density), and symptom-rhythm correlation (SRC) were assessed. In total, 484 patients (60% male, 62 ± 9.9 years) were included. Adherence, motivation, and patient satisfaction were high. %AF recordings, AF load, and %AF days (rs = 0.88-0.95) and %symptom recordings, symptom load, and %symptom days (rs = 0.95-0.98) showed positive correlations. The SRC correlated negatively with time spent with symptoms (rs = -0.65-0.90) and with time spent with AF (rs = -0.31-0.34). In patients with paroxysmal AF before ablation and AF during the monitoring period, 87% (n = 39/44) had a low-density score <50% ('paroxysmal AF pattern') while 5% (n = 2/44) had a high-density score >90% ('persistent AF pattern'). Corresponding numbers for patients with persistent AF before ablation were 48% (n = 11/23) and 43% (n = 10/23), respectively. CONCLUSION: On-demand, app-based simultaneous rhythm and symptom assessment provides objective proxies of time spent with AF and/or symptoms and SRC, which may assist in assessing AF and symptom outcomes after AF ablation.

Dimensional emotion recognition from camera-based PRV features.

Heart rate variability (HRV) is an important indicator of autonomic nervous system activity and can be used for the identification of affective states. The development of remote Photoplethysmography (rPPG) technology has made it possible to measure pulse rate variability (PRV) using a camera without any sensor-skin contact, which is highly correlated to HRV, thus, enabling contactless assessment of emotional states. In this study, we employed ten machine learning techniques to identify emotions using camera-based PRV features. Our experimental results show that the best classification model achieved a coordination correlation coefficient of 0.34 for value recognition and 0.36 for arousal recognition. The rPPG-based measurement has demonstrated promising results in detecting HAHV (high-arousal high-valence) emotions with high accuracy. Furthermore, for emotions with less noticeable variations, such as sadness, the rPPG-based measure outperformed the baseline deep network for facial expression analysis.

Melanin bias in pulse oximetry explained by light source spectral bandwidth.

BACKGROUND: Pulse oximetry uses noninvasive optical measurements of light transmission from each of two sources through vascularised living tissue over the cardiac cycle (SpO2). From those measurements, the relative amount of oxygenated haemoglobin (SaO2) in circulating blood can be deduced. Recent reports have shown that, compared with SaO2 measurements from blood samples, SpO2 measurements are biased erroneously high for patients with dark skin. METHODS: We developed a new method, spectrally resolved photoplethysmography (srPPG), to examine how spectral bandwidth affects the transmission of polychromatic light through the fingertip across the cardiac cycle. We measured and recorded the spectral transmission through the fingertip as the O2 concentration in inspired air was reduced. We applied digital spectral filters of two different bandwidths, narrow or broad, to the same srPPG recordings to determine whether SpO2 readings systematically varied for the two bandwidths. The srPPG method also allowed us to measure the fractional amount of melanin in the optical path. The effect of melanin content on the ratio of SpO2 readings for narrow and broad spectral bandwidths was analysed. RESULTS: We hypothesised, based upon the Beer-Lambert law, and then showed experimentally, that the light emission spectra of light-emitting diode light sources, as used in commercial pulse oximeters, result in erroneously high SpO2 measurements for patients having greater melanin concentrations in their skin than those of the subject pool used for instrument calibration. CONCLUSIONS: To eliminate melanin bias, pulse oximeters should use much narrower spectral bandwidths than those used in current models.

Evaluation of an algorithm-guided photoplethysmography for atrial fibrillation burden using a smartwatch.

BACKGROUND: Wearable devices based on the PPG algorithm can detect atrial fibrillation (AF) effectively. However, further investigation of its application on long-term, continuous monitoring of AF burden is warranted. METHOD: The performance of a smartwatch with continuous photoplethysmography (PPG) and PPG-based algorithms for AF burden estimation was evaluated in a prospective study enrolling AF patients admitted to Beijing Anzhen Hospital for catheter ablation from September to November 2022. A continuous Electrocardiograph patch (ECG) was used as the reference device to validate algorithm performance for AF detection in 30-s intervals. RESULTS: A total of 578669 non-overlapping 30-s intervals for PPG and ECG each from 245 eligible patients were generated. An interval-level sensitivity of PPG was 96.3% (95% CI 96.2%-96.4%), and specificity was 99.5% (95% CI 99.5%-99.6%) for the estimation of AF burden. AF burden estimation by PPG was highly correlated with AF burden calculated by ECG via Pearson correlation coefficient (R2 = 0.996) with a mean difference of -0.59 (95% limits of agreement, -7.9% to 6.7%). The subgroup study showed the robust performance of the algorithm in different subgroups, including heart rate and different hours of the day. CONCLUSION: Our results showed the smartwatch with an algorithm-based PPG monitor has good accuracy and stability in continuously monitoring AF burden compared with ECG patch monitors, indicating its potential for diagnosing and managing AF.

Diagnostic accuracy of apple watch ECG outputs in identifying dysrhythmias: A comparison with 12-Lead ECG in emergency department.

BACKGROUND: Wearable devices, particularly smartwatches like the Apple Watch (AW), can record important cardiac information, such as single­lead electrocardiograms (ECGs). Although they are increasingly used to detect conditions such as atrial fibrillation (AF), research on their effectiveness in detecting a wider range of dysrhythmias and abnormal ECG findings remains limited. The primary objective of this study is to evaluate the accuracy of the AW in detecting various cardiac rhythms by comparing it with standard ECG's lead-I. METHODS: This single-center prospective observational study was conducted in a tertiary care emergency department (ED) between 1.10.2023 and 31.10.2023. The study population consisted of all patients assessed in the critical care areas of the ED, all of whom underwent standard 12­lead ECGs for various clinical reasons. Participants in the study were included consecutively. An AW was attached to patients' wrists and an ECG lead-I printout was obtained. Heart rate, rhythm and abnormal findings were evaluated and compared with the lead-I of standard ECG. Two emergency medicine specialists performed the ECG evaluations. Rhythms were categorized as normal sinus rhythm and abnormal rhythms, while ECG findings were categorized as the presence or absence of abnormal findings. AW and 12­lead ECG outputs were compared using the McNemar test. Predictive performance analyses were also performed for subgroups. Bland-Altman analysis using absolute mean differences and concordance correlation coefficients was used to assess the level of heart rate agreement between devices. RESULTS: The study was carried out on 721 patients. When analyzing ECG rhythms and abnormal findings in lead-I, the effectiveness of AW in distinguishing between normal and abnormal rhythms was similar to standard ECGs (p = 0.52). However, there was a significant difference between AW and standard ECGs in identifying abnormal findings in lead-I (p < 0.05). Using Bland-Altman analysis for heart rate assessment, the absolute mean difference for heart rate was 0.81 ± 6.12 bpm (r = 0.94). There was strong agreement in 658 out of 700 (94%) heart rate measurements. CONCLUSION: Our study indicates that the AW has the potential to detect cardiac rhythms beyond AF. ECG tracings obtained from the AW may help evaluate cardiac rhythms prior to the patient's arrival in the ED. However, further research with a larger patient cohort is essential, especially for specific diagnoses.

Feasibility and accuracy of noninvasive continuous hemoglobin monitoring using transesophageal photoplethysmography in porcine model.

BACKGROUND: Continuous and noninvasive hemoglobin (Hb) monitoring during surgery is essential for anesthesiologists to make transfusions decisions. The aim of this study was to investigate the feasibility and accuracy of noninvasive and continuous Hb monitoring using transesophageal descending aortic photoplethysmography (dPPG) in porcine model. METHODS: Nineteen landrace pigs, aged 3 to 5 months and weighing 30 to 50 kg, were enrolled in this study. A homemade oximetry sensor, including red (660 nm) and infrared (940 nm) lights, was placed in the esophagus for dPPG signal detection to pair with the corresponding reference Hb values (Hbi-STAT) measured by blood gas analysis. The decrease and increase changes in Hb concentration were achieved by hemodilution and transfusion. Metrics, including alternating current (AC), direct current (DC), and AC/DC for both red and infrared light were extracted from the dPPG signal. A receiver operating characteristic (ROC) curve was built to evaluate the performance of dPPG metrics in predicting the Hb "trigger threshold" of transfusion (Hb < 60 g/L and Hb > 100 g/L). Agreement and trending ability between Hb measured by dPPG (HbdPPG) and by blood gas analysis were analyzed by Bland-Altman method and polar plot graph. Error grid analysis was also performed to evaluate clinical significance of HbdPPG measurement. RESULTS: The dPPG signal was successfully detected in all of the enrolled experimental pigs, without the occurrence of a continuous loss of dPPG signal for 2 min during the entire measurement. A total of 376 pairs of dPPG signal and Hbi-STAT were acquired. ACred/DCred and ACinf/DCinf had moderate correlations with Hbi-STAT, and the correlation coefficients were 0.790 and 0.782, respectively. The areas under the ROC curve for ACred/DCred and ACinf/DCinf in predicting Hbi-STAT < 60 g/L were 0.85 and 0.75, in predicting Hbi-STAT > 100 g/L were 0.90 and 0.83, respectively. Bland-Altman analysis and polar plot showed a small bias (1.69 g/L) but a wide limit of agreement (-26.02-29.40 g/L) and a poor trend ability between HbdPPG and Hbi-STAT. Clinical significance analysis showed that 82% of the data lay within the Zone A, 18% within the Zone B, and 0% within the Zone C. CONCLUSION: It is feasible to establish a noninvasive and continuous Hb monitoring by transesophageal dPPG signal. The ACred/DCred extracted from the dPPG signal could provide a sensitive prediction of the Hb threshold for transfusion. The Hb concentration measured by dPPG signal has a moderate correlation with that measured by blood gas analysis. This animal study may provide an experimental basis for the development of bedside HbdPPG monitoring in the future.

Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits.

Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.

This review explores cuffless blood pressure technologies and their impact on clinical practice, highlighting innovative devices that offer non-invasive, continuous and non-continuous monitoring without a cuff. Signal processing is essential for ensuring accurate readings, as it filters out unwanted artifacts and environmental disturbances which could make the reading inaccurate. While these advancements show great potential for transforming cardiovascular care, there are still several challenges to overcome, including the need for standardized protocols and validation studies to ensure their reliability and safety in clinical settings. Collaborative efforts between engineers, clinicians, and regulatory bodies are needed to address the technical and regulatory complexities surrounding the implementation of these technologies. These cuffless blood pressure measurement devices have the potential to reshape how we understand and manage blood pressure and hypertension.

Impact of Skin Pigmentation on Pulse Oximetry Blood Oxygenation and Wearable Pulse Rate Accuracy: Systematic Review and Meta-Analysis.

BACKGROUND: Photoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO2) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities. OBJECTIVE: This systematic review and meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO2 and PR according to skin pigmentation. METHODS: We identified 23 pulse oximetry studies (n=59,684; 197,353 paired SpO2-arterial blood observations) and 4 wearable PR studies (n=176; 140,771 paired PPG-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO2 accuracy root-mean-square values to the regulatory threshold of 3% and PR 95% limits of agreement values to +5 or -5 beats per minute (bpm), according to the standards of the American National Standards Institute, Association for the Advancement of Medical Instrumentation, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% CI. RESULTS: For SpO2, accuracy root-mean-square values were 3.96%, 4.71%, and 4.15%, and pooled mean biases were 0.70% (95% CI 0.17%-1.22%), 0.27% (95% CI -0.64% to 1.19%), and 1.27% (95% CI 0.58%-1.95%) for light, medium, and dark pigmentation, respectively. For PR, 95% limits of agreement values were from -16.02 to 13.54, from -18.62 to 16.84, and from -33.69 to 32.54, and pooled mean biases were -1.24 (95% CI -5.31 to 2.83) bpm, -0.89 (95% CI -3.70 to 1.93) bpm, and -0.57 (95% CI -9.44 to 8.29) bpm for light, medium, and dark pigmentation, respectively. CONCLUSIONS: SpO2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching U.S. Food and Drug Administration guidance and industry standard thresholds. Pulse oximeters significantly overestimate SpO2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation.

Plethysmography: A game changer for chronic venous insufficiency diagnosis, treatment, and follow-up.

OBJECTIVE: Chronic Venous Insufficiency (CVI) presents a various clinical symptoms and treatment options. Although it is generally known which treatment option is more appropriate in which situations in CVI, it is even more difficult to find the right option in some uncertain situations. In this study, we aimed to evaluate the potential contribution of Digital Photoplethysmography (D-PPG) in the diagnosis of CVI and especially in the selection of treatment options. PATIENTS AND METHOD: This retrospective study was conducted at Bursa Heart and Arrhythmia Hospital, involving 721 consecutive patients diagnosed with CVI and with Endovenous Laser Ablation (EVLA) indication. The patients were divided into 2 groups according to the extent of the failure in Doppler USG. In Group 1, the insufficiency ended above the knee, and in Group 2, the insufficiency progressed to below the knee. Patients were evaluated based on anamnesis, physical examination, Doppler USG, and D-PPG. Clinical classification, Venous Clinic Severity Score (VCSS), Quality of Life (QoL) assessment, venous pump capacity (VPC), and venous refill time (VRT) were measured. RESULTS: The study included 263 male and 458 female patients with a mean age of 52.37 ± 12.26 years. Significant differences were observed between Group 1 (above knee reflux) and Group 2 (below knee reflux) patients in terms of VCSS, QoL, VPC, and VRT values. The mean values of VCSS, patient complaints, VPC, and VRT were higher in Group 2 patients. Similar findings were observed within the CEAP 2 subgroup. CONCLUSION: D-PPG shows potential as a valuable tool in the diagnosis and treatment of CVI. By providing information about venous hemodynamics and volume changes, it can assist in optimizing treatment decisions, including saphenous vein preservation. Combining D-PPG with Doppler USG may improve the comprehensive assessment of CVI and change the treatment option, especially for CEAP 2 patients. More research is needed to confirm these findings and explore wider applications of plethysmographic methods in the management of CVI.

InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.

BACKGROUND: This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monitoring with wearable devices. Furthermore, our aim was to develop a more efficient sleep monitoring method by considering both the interpretability and uncertainty of the model's prediction results, with the goal of providing support to medical professionals in their decision-making process. METHOD: The developed 4-class sleep staging model based on continuous PPG data incorporates several key components: a local attention module, an InceptionTime module, a time-distributed dense layer, a temporal convolutional network (TCN), and a 1D convolutional network (CNN). This model prioritizes both interpretability and uncertainty estimation in its prediction results. The local attention module is introduced to provide insights into the impact of each epoch within the continuous PPG data. It achieves this by leveraging the TCN structure. To quantify the uncertainty of prediction results and facilitate selective predictions, an energy score estimation is employed. By enhancing both the performance and interpretability of the model and taking into consideration the reliability of its predictions, we developed the InsightSleepNet for accurate sleep staging. RESULT: InsightSleepNet was evaluated using three distinct datasets: MESA, CFS, and CAP. Initially, we assessed the model's classification performance both before and after applying an energy score threshold. We observed a significant improvement in the model's performance with the implementation of the energy score threshold. On the MESA dataset, prior to applying the energy score threshold, the accuracy was 84.2% with a Cohen's kappa of 0.742 and weighted F1 score of 0.842. After implementing the energy score threshold, the accuracy increased to a range of 84.8-86.1%, Cohen's kappa values ranged from 0.75 to 0.78 and weighted F1 scores ranged from 0.848 to 0.861. In the case of the CFS dataset, we also noted enhanced performance. Before the application of the energy score threshold, the accuracy stood at 80.6% with a Cohen's kappa of 0.72 and weighted F1 score of 0.808. After thresholding, the accuracy improved to a range of 81.9-85.6%, Cohen's kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.821 to 0.857. Similarly, on the CAP dataset, the initial accuracy was 80.6%, accompanied by a Cohen's kappa of 0.73 and weighted F1 score was 0.805. Following the application of the threshold, the accuracy increased to a range of 81.4-84.3%, Cohen's kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.813 to 0.842. Additionally, by interpreting the model's predictions, we obtained results indicating a correlation between the peak of the PPG signal and sleep stage classification. CONCLUSION: InsightSleepNet is a 4-class sleep staging model that utilizes continuous PPG data, serves the purpose of continuous sleep monitoring with wearable devices. Beyond its primary function, it might facilitate in-depth sleep analysis by medical professionals and empower them with interpretability for intervention-based predictions. This capability can also support well-informed clinical decision-making, providing valuable insights and serving as a reliable second opinion in medical settings.

First-in-Human Study for Evaluating the Accuracy of Smart Ring Based Cuffless Blood Pressure Measurement.

BACKGROUND: Recently, a ring-type cuffless blood pressure (BP) measuring device has been developed. This study was a prospective, single arm, first-in-human pivotal trial to evaluate accuracy of BP measurement by the new device. METHODS: The ring-type smart wearable monitoring device measures photoplethysmography signals from the proximal phalanx and transmits the data wirelessly to a connected smartphone. For the BP comparison, a cuff was worn on the arm to check the reference BP by auscultatory method, while the test device was worn on the finger of the opposite arm to measure BP simultaneously. Measurements were repeated for up to three sets each on the left and right arms. The primary outcome measure was mean difference and standard deviation of BP differences between the test device and the reference readings. RESULTS: We obtained 526 sets of systolic BP (SBP) and 513 sets of diastolic BP (DBP) from 89 subjects, with ranges of 80 to 175 mmHg and 43 to 122 mmHg for SBP and DBP, respectively. In sample-wise comparison, the mean difference between the test device and the reference was 0.16 ± 5.90 mmHg (95% limits of agreement [LOA], -11.41, 11.72) in SBP and -0.07 ± 4.68 (95% LOA, -9.26, 9.10) in DBP. The test device showed a strong correlation with the reference for SBP (r = 0.94, P < 0.001) and DBP (r = 0.95, P < 0.001). There were consistent results in subject-wise comparison. CONCLUSION: The new ring-type BP measuring device showed a good correlation for SBP and DBP with minimal bias compared with an auscultatory method.

A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software.

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates' determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman's coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman's coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

Quality-Aware Signal Processing Mechanism of PPG Signal for Long-Term Heart Rate Monitoring.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

Advanced Necklace for Real-Time PPG Monitoring in Drivers.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data.

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.

Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction.

Oxygen uptake (V˙O2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with V˙O2 and is often used for indirect V˙O2 prediction. This study investigates the impact of HR placement on V˙O2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (V˙E) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V˙O2, RESP, V˙E, and HR were collected and used to develop a neural network model for V˙O2 prediction. The influence of HR position on prediction accuracy was assessed via Bland-Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that HR combined with RESP and V˙E (V˙O2HR+RESP+V˙E) produces the most accurate V˙O2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V˙O2 prediction decreases, particularly within high-intensity exercise. The substitution of HR with different anatomical sites significantly impacts V˙O2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering HR placement in V˙O2 prediction models, with RESP and V˙E serving as effective compensatory factors. These findings contribute to refining indirect V˙O2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements.