A Wireless Power Transfer Mattress Based System for Perpetually Operating Physiological Monitoring Wearables.

This article presents a novel wireless power mattress-based system architecture tailored to guarantee continuous energy for in-home environment healthcare wearables intended to be used in the context of patients who would benefit from long-term monitoring of specific physiological biomarkers. The design demonstrates that it is possible to transfer over 20 mW at a primary-secondary distance of 20.7 cm, whilst still keeping within all FCC/ICNIRP safety regulations, using the proposed simplified beamforming-controlled power transfer multi-input single-output system. Compared with other beamforming-controlled based works, the proposed design used non-coupling coil arrays, significantly reducing the algorithmic complexity. An on-chip wireless power charger system was also designed to provide high-efficiency power storage (89.3% power conversion efficiency and 83.9% power charge efficiency), guaranteeing wearables can continuously maintain their functionality. In contrast with conventional NiMh chargers, this work proposes a trimming function that makes it compatible with batteries of varying capacities. It also employs a four-stage charge loop to ensure safety and sustainability during the charging process. Overall, this work shows that by relying on wireless power transfer, it is, in principle, possible to create a safe wearable that could enable continuous monitoring of certain healthcare biomarkers with little or zero maintenance burden for the patients or carers.

A comparative study in class imbalance mitigation when working with physiological signals.

Class imbalance is a common challenge that is often faced when dealing with classification tasks aiming to detect medical events that are particularly infrequent. Apnoea is an example of such events. This challenge can however be mitigated using class rebalancing algorithms. This work investigated 10 widely used data-level class imbalance mitigation methods aiming towards building a random forest (RF) model that attempts to detect apnoea events from photoplethysmography (PPG) signals acquired from the neck. Those methods are random undersampling (RandUS), random oversampling (RandOS), condensed nearest-neighbors (CNNUS), edited nearest-neighbors (ENNUS), Tomek's links (TomekUS), synthetic minority oversampling technique (SMOTE), Borderline-SMOTE (BLSMOTE), adaptive synthetic oversampling (ADASYN), SMOTE with TomekUS (SMOTETomek) and SMOTE with ENNUS (SMOTEENN). Feature-space transformation using PCA and KernelPCA was also examined as a potential way of providing better representations of the data for the class rebalancing methods to operate. This work showed that RandUS is the best option for improving the sensitivity score (up to 11%). However, it could hinder the overall accuracy due to the reduced amount of training data. On the other hand, augmenting the data with new artificial data points was shown to be a non-trivial task that needs further development, especially in the presence of subject dependencies, as was the case in this work.

Feature evaluation of accelerometry signals for cough detection.

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector.

Validation of a Wearable Medical Device for Automatic Diagnosis of OSA against Standard PSG.

STUDY OBJECTIVE: The objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis. MATERIAL AND METHODS: This observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022. RESULTS: A total of 80 patients were recruited for the trial. The patients had a median Epworth scoring of 10, a mean of 10.4, and a range of 0-24. The mean AHI obtained with PSG plus sleep clinician marking was 23.2, median 14.3 and range 0-108. The study demonstrated a diagnostic accuracy (based on AHI) of 95.24%, sensitivity of 92.86%, specificity of 97.14%, positive predictive value of 96.30%, negative predictive value of 94.44%, positive likelihood ratio of 32.50 and negative likelihood ratio of 0.07. CONCLUSIONS: The AcuPebble SA100 (EU) device has demonstrated an accurate automated diagnosis of OSA in patients undergoing in-clinic sleep testing when compared against the gold-standard reference of in-clinic PSG.

Validation of Tracheal Sound-Based Respiratory Effort Monitoring for Obstructive Sleep Apnoea Diagnosis.

Background: Respiratory effort is considered important in the context of the diagnosis of obstructive sleep apnoea (OSA), as well as other sleep disorders. However, current monitoring techniques can be obtrusive and interfere with a patient's natural sleep. This study examines the reliability of an unobtrusive tracheal sound-based approach to monitor respiratory effort in the context of OSA, using manually marked respiratory inductance plethysmography (RIP) signals as a gold standard for validation. Methods: In total, 150 patients were trained on the use of type III cardiorespiratory polygraphy, which they took to use at home, alongside a neck-worn AcuPebble system. The respiratory effort channels obtained from the tracheal sound recordings were compared to the effort measured by the RIP bands during automatic and manual marking experiments. A total of 133 central apnoeas, 218 obstructive apnoeas, 263 obstructive hypopneas, and 270 normal breathing randomly selected segments were shuffled and blindly marked by a Registered Polysomnographic Technologist (RPSGT) in both types of channels. The RIP signals had previously also been independently marked by another expert clinician in the context of diagnosing those patients, and without access to the effort channel of AcuPebble. The classification achieved with the acoustically obtained effort was assessed with statistical metrics and the average amplitude distributions per respiratory event type for each of the different channels were also studied to assess the overlap between event types. Results: The performance of the acoustic effort channel was evaluated for the events where both scorers were in agreement in the marking of the gold standard reference channel, showing an average sensitivity of 90.5%, a specificity of 98.6%, and an accuracy of 96.8% against the reference standard with blind expert marking. In addition, a comparison using the Embla Remlogic 4.0 automatic software of the reference standard for classification, as opposed to the expert marking, showed that the acoustic channels outperformed the RIP channels (acoustic sensitivity: 71.9%; acoustic specificity: 97.2%; RIP sensitivity: 70.1%; RIP specificity: 76.1%). The amplitude trends across different event types also showed that the acoustic channels exhibited a better differentiation between the amplitude distributions of different event types, which can help when doing manual interpretation. Conclusions: The results prove that the acoustically obtained effort channel extracted using AcuPebble is an accurate, reliable, and more patient-friendly alternative to RIP in the context of OSA.

A Multilayer Monte Carlo Analysis of Optical Interactions in Reflectance Neck Photoplethysmography.

This paper presents a multilayer Monte Carlo model of a healthy human neck to investigate the light-tissue interaction during different perfusion states within its dermal layer. Whilst there is great interest in advancing wearable technologies for medical applications, and non-invasive techniques like photoplethysmography (PPG) have been studied in detail, research has focused on more conventional body regions like the finger, wrist, and ear. Alternatively, the neck could offer access to additional physiological parameters which other body regions are unsuitable for. The aim of this work was to investigate the effects of several factors that would influence the optimum design of a reflectance PPG sensor for the neck. These included the source-detector separation on the optical path, penetration depth, and light detection efficiency. The results were generated from a static multilayer model in a reflectance mode geometry at two wavelengths, 660 nm and 880 nm, containing different blood volume fractions with a fixed oxygen saturation. Simulations indicated that both wavelengths penetrated similar depths, where optimal source-detector separation should not exceed 3 mm or 2.4 mm, for red and infrared respectively. Within this range, light interrogates the dermal-fat boundary corresponding to the last neck tissue layer positively contributing to a neck PPG acquisition.

Proof-of-concept of a mattress based power harvesting system architecture suitable for wireless physiological monitoring systems.

This work investigates the feasibility of having a mattress based wireless power transfer system with transfer efficiency such that the received power could potentially be enough to fully power up wearable systems intended to provide some level of continuous physiological monitoring; hence eliminating the need for users to ever have to recharge the systems. The novel architecture proposed in this work, to optimise power transfer efficiency against angular misalignment typical of non-static use is based on a non-coupling coil structure combined with a magnetic beamforming scheme. The coil system also incorporates a non-coupling relay array to overcome the significant loss in power transfer efficiency associated to increasing distances between transmitters and receivers. The system is proven to be able to deliver around 11.8mW of power in the worst-case scenario, with a receiver 25cm above the transmitters, whilst meeting the safety requirements associated to electromagnetic exposure to the human body.

Assessing the Feasibility of Acoustic Based Seizure Detection.

OBJECTIVE: Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device. METHODS: Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes. RESULTS: Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of -42 s. CONCLUSION: The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. SIGNIFICANCE: The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only.

Performance Evaluation of Embedded Image Classification Models Using Edge Impulse for Application on Medical Images.

This work explores the possibility of applying edge machine learning technology in the context of portable medical image diagnostic systems. This was done by evaluating the performance of two machine learning (ML) algorithms, that are widely used on medical images, embedding them into a resource-constraint Nordic nrf52840 microcontroller. The first model was based on transfer learning of the MobileNetVI architecture. The second was based on a convolutional neural network (CNN) with three layers. The Edge Impulse platform was used for training and deploying the embedded machine learning algorithms. The models were deployed as a C++ library for both, a 32-bit floating point representation and an 8-bit fixed integer representation. The inference on the microcontroller was evaluated under four different cases each, using the Edge Impulse EON compiler in one case, and the Tensor Flow Lite (TFLite) interpreter in the second. Results reported include the memory footprint (RAM, and Flash), classification accuracy, time for inference, and power consumption.

Automatic Identification of Snoring and Groaning Segments in Acoustic Recordings.

Sleep-related breathing disorders have severe impact on the quality of lives of those suffering from them. These disorders present with a variety of symptoms, out of which snoring and groaning are very common. This paper presents an algorithm to identify and classify segments of acoustic respiratory sound recordings that contain both groaning and snoring events. The recordings were obtained from a database containing 20 subjects from which features based on the Mel-frequency cepstral coefficients (MFCC) were extracted. In the first stage of the algorithm, segments of recordings consisting of either snoring or groaning episodes - without classifying them - were identified. In the second stage, these segments were further differentiated into individual groaning or snoring events. The algorithm in the first stage achieved a sensitivity and specificity of 90.5% ±2.9% and 90.0% ±1.6% respectively, using a RUSBoost model. In the second stage, a random forest classifier was used, and the accuracies for groan and snore events were 78.1% ±4.7% and 78.4% ±4.7% respectively.

Artifacts classification and apnea events detection in neck photoplethysmography signals.

The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).

Sleep Posture Detection Using an Accelerometer Placed on the Neck.

Sleep position monitoring is key when attempting to address posture triggered sleep disorders. Many studies have explored sleep posture detection from a dedicated physical sensing channel exploiting optimum body locations, such as the torso; or alternatively non-contact approaches. But, little work has been done to try to detect sleep position from a body location which, whilst being suboptimal for that purpose, does however allow for better extraction of more critical biomarkers from other sensing modalities, making possible multi-modal monitoring in certain clinical applications. This work presents two different approaches, at varying levels of complexity, for detecting 4 main sleep positions (supine, prone, lateral right and lateral left) from accelerometry data obtained by a single wearable device placed on the neck. An ultra light-weight threshold-based model is presented in this work, in addition to an Extra-Trees classifier. The threshold-based model was able to achieve 95% average accuracy and 0.89 F1-score on out-of-sample data, showing that it is possible to obtain a moderately high classification performance using a simple rule-based model. The ExtraTrees classifier, on the other hand, was able to achieve 99 % average accuracy and 0.99 average F1-score using only 25 base estimators with maximum depth of 20. Both models show promise in detecting sleep posture with high accuracy when collecting the signals from a neck-worn accelerometer sensor.

Analysis of the factors affecting the adoption and compliance of the NHS COVID-19 mobile application: a national cross-sectional survey in England.

OBJECTIVES: To conduct an independent study investigating how adults perceive the usability and functionality of the 'National Health Service (NHS) COVID-19' application (app). This study aims to highlight strengths and provide recommendations to improve adoption of future contact tracing developments. DESIGN: A 60-item, anonymous online questionnaire, disseminated through social media outlets and email lists by a team from Imperial College London. SETTING: England. PARTICIPANTS: Convenience sample of 1036 responses, from participants aged 18 years and above, between December 2020 and January 2021. PRIMARY OUTCOME MEASURES: Evaluate the compliance and public attitude towards the 'NHS COVID-19' app regarding its functionality and features. This included whether participants' expectations were met, and their thoughts on the app privacy and security. Furthermore, to distinguish how usability, perception, and adoption differed with varying demographics and user values. RESULTS: Fair compliance with the app features was identified, meeting expectations of the 62.1% of participants who stated they downloaded it after weighted analysis. However, participants finding the interface challenging were less likely to read information in the app and had a lesser understanding of its functionality. Furthermore, little understanding regarding the app's functionality and privacy concerns was a possible reason why users did not download it. A readability analysis of the text revealed information within the app was conveyed at a level that may be too complex for up to 43% of the UK population. The study highlighted issues related to the potential of false positives caused by the design choices in the 'Check-In' feature. CONCLUSION: This study showed that while the 'NHS COVID-19' app was viewed positively, there remained issues regarding participants' perceived knowledge of app functionality, potentially affecting compliance. Therefore, we recommended improvements regarding the delivery and presentation of the app's information, and highlighted the potential need for the ability to check out of venues to reduce the number of false positive contacts.

Towards Automatic Identification of Epileptic Recordings in Long-term EEG Monitoring.

Electroencephalogram (EEG) is a crucial tool in the diagnosis and management of epilepsy. The process of analyzing EEG is time consuming leading to the development of seizure detection algorithms to aid its analysis. This approach is limited since it requires seizures to occur during monitoring periods and can often lead to misdiagnosis in cases where seizure occurrence is rare. For such cases, it has been shown that the interictal periods in EEG signals, which is the predominant state in long-term monitoring, can be useful for the diagnosis of epilepsy. This paper presents an algorithm, using the information in interictal periods, to discriminate between long-term EEG recordings of epilepsy patients and healthy subjects. It extracts several time and frequency-time domain features from the signals and classifies them using an ensemble classifier, achieving 100% sensitivity and 98.7% specificity in classifying 267 recordings from 105 subjects. The results demonstrate the feasibility of this approach to reliably identify EEG recordings of epilepsy subjects automatically which can be highly useful to facilitate screening and diagnosis of epilepsy, especially in those parts of the world where there is a lack of trained personnel for interpreting EEG signals.

Effects of denoising strategies on R-wave detection in ECG analysis.

The use of ECG in cardiovascular health monitoring is well established. The signal is collected using specialised equipment, capturing the electrical discharge properties of the human heart. This produces a well-structured signal trace, which can be characterised through its peaks and troughs. The signal can then be used by clinicians to diagnose cardiac disorders. However, as with any measuring equipment, the ECG output signal can experience deterioration resulting from noise. This can happen due to environmental interference, human issues or measuring equipment failure, necessitating the development of various denoising strategies to reduce, or remove, the noise. In this paper, we study typically occurring types of noise and implement popular strategies used to rectify them. We also show, that the given strategy's denoising potential is directly related to R-wave detection, and provide best strategies to apply when faced with specific noise type.

Accuracy and usability of AcuPebble SA100 for automated diagnosis of obstructive sleep apnoea in the home environment setting: an evaluation study.

OBJECTIVES: Obstructive sleep apnoea (OSA) is a heavily underdiagnosed condition, which can lead to significant multimorbidity. Underdiagnosis is often secondary to limitations in existing diagnostic methods. We conducted a diagnostic accuracy and usability study, to evaluate the efficacy of a novel, low-cost, small, wearable medical device, AcuPebble_SA100, for automated diagnosis of OSA in the home environment. SETTINGS: Patients were recruited to a standard OSA diagnostic pathway in an UK hospital. They were trained on the use of type-III-cardiorespiratory polygraphy, which they took to use at home. They were also given AcuPebble_SA100; but they were not trained on how to use it. PARTICIPANTS: 182 consecutive patients had been referred for OSA diagnosis in which 150 successfully completed the study. PRIMARY OUTCOME MEASURES: Efficacy of AcuPebble_SA100 for automated diagnosis of moderate-severe-OSA against cardiorespiratory polygraphy (sensitivity/specificity/likelihood ratios/predictive values) and validation of usability by patients themselves in their home environment. RESULTS: After returning the systems, two expert clinicians, blinded to AcuPebble_SA100's output, manually scored the cardiorespiratory polygraphy signals to reach a diagnosis. AcuPebble_SA100 generated automated diagnosis corresponding to four, typically followed, diagnostic criteria: Apnoea Hypopnoea Index (AHI) using 3% as criteria for oxygen desaturation; Oxygen Desaturation Index (ODI) for 3% and 4% desaturation criteria and AHI using 4% as desaturation criteria. In all cases, AcuPebble_SA100 matched the experts' diagnosis with positive and negative likelihood ratios over 10 and below 0.1, respectively. Comparing against the current American Academy of Sleep Medicine's AHI-based criteria demonstrated 95.33% accuracy (95% CI (90·62% to 98·10%)), 96.84% specificity (95% CI (91·05% to 99·34%)), 92.73% sensitivity (95% CI (82·41% to 97·98%)), 94.4% positive-predictive value (95% CI (84·78% to 98·11%)) and 95.83% negative-predictive value (95% CI (89·94% to 98·34%)). All patients used AcuPebble_SA100 correctly. Over 97% reported a strong preference for AcuPebble_SA100 over cardiorespiratory polygraphy. CONCLUSIONS: These results validate the efficacy of AcuPebble_SA100 as an automated diagnosis alternative to cardiorespiratory polygraphy; also demonstrating that AcuPebble_SA100 can be used by patients without requiring human training/assistance. This opens the doors for more efficient patient pathways for OSA diagnosis. TRIAL REGISTRATION NUMBER: NCT03544086; ClinicalTrials.gov.

Characterization of Artifact Signals in Neck Photoplethysmography.

OBJECTIVE: The neck is a very attractive measurement location for multimodal physiological monitoring, since it offers the possibility of extracting clinically relevant parameters, which cannot be obtained from other body locations, such as lung volumes. It is for this reason that obtaining PPG from the neck would be of interest. PPG signals, however, are very susceptible to artifacts which greatly compromise their quality. But the extent of this is going to depend on, the nature of the artifacts and the strength of the sensed signal, both of which are location dependent. This paper presents for the first time the characterization of artifacts affecting neck PPG signals. METHODS: Neck PPG data was recorded from 19 participants, who performed ten different activities to deliberately introduce common artifacts. 41 PPG features were extracted and statistically analyzed to investigate which ones showed the greatest ability to differentiate normal PPG from each artifact. A customized minimum Redundancy Maximum Relevance (mRMR) feature selection approach was implemented, to select the top 10 features. RESULTS: Artifacts caused by Swallowing, Yawning and Coughing exhibited larger Spectral Entropy, Average Power and smaller Spectral Kurtosis, than normal PPG. Head movement artifacts, also demonstrated highly disordered and noisy frequency spectra, and were characterized by having larger and irregular time domain features. In addition, the analysis showed that different respiratory states that could be of clinical interests, such as presence of apneas, were also distinguishable from sources of interference. SIGNIFICANCE: These findings are important for the development of PPG denoising algorithms and subsequent obtention of biomarkers of interest, or alternatively for applications where the events of interest are the artifacts themselves.

Extracting the Jugular Venous Pulse from Anterior Neck Contact Photoplethysmography.

The jugular venous pulse (JVP) is the reference physiological signal used to detect right atrial and central venous pressure (CVP) abnormalities in cardio-vascular diseases (CVDs) diagnosis. Invasive central venous line catheterization has always been the gold standard method to extract it reliably. However, due to all the risks it entails, novel non-invasive approaches, exploiting distance cameras and lasers, have recently arisen to measure the JVP at the external and internal jugular veins. These remote options however, constraint patients to very specific body positions in front of the imaging system, making it inadequate for long term monitoring. In this study, we demonstrate, for the first time, that reflectance photoplethysmography (PPG) can be an alternative for extracting the JVP from the anterior jugular veins, in a contact manner. Neck JVP-PPG signals were recorded from 20 healthy participants, together with reference ECG and arterial finger PPG signals for validation. B-mode ultrasound imaging of the internal jugular vein also proved the validity of the proposed method. The results show that is possible to identify the characteristic a, c, v pressure waves in the novel signals, and confirm their cardiac-cycle timings in consistency with established cardiac physiology. Wavelet coherence values (close to 1 and phase shifts of ±180°) corroborated that neck contact JVP-PPG pulses were negatively correlated with arterial finger PPG. Average JVP waveforms for each subject showed typical JVP pulses contours except for the singularity of an unknown "u" wave occurring after the c wave, in half of the cohort. This work is of great significance for the future of CVDs diagnosis, as it has the potential to reduce the risks associated with conventional catheterization and enable continuous non-invasive point-of-care monitoring of CVP, without restricting patients to limited postures.

Performance metrics for the accurate characterisation of interictal spike detection algorithms.

Automated spike detection methods for the epileptic EEG are highly desired to speed up and disambiguate EEG analysis. However, it is difficult to accurately and concisely present the performance of such algorithms due to the large number of recording and algorithm variables that must be accounted for. This paper summarizes the core variables involved and presents different methods for calculating the average performance. These methods incorporate weighting factors to correct for non-ideal test cases. The factors are found to have a significant effect on the appearance of the results and the performance level that the algorithm appears to achieve. Four different weighting factors are considered and a duration divided by the number of events weighting is recommended for use in future studies.

A Novel Algorithm for HRV Estimation from Short-Term Acoustic Recordings at Neck.

Heart rate variability (HRV) is an important noninvasive parameter to monitor the activity of the autonomic nervous system. This paper proposes an algorithm to analyze HRV by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at neck) of an adult subject, primarily intended to record breathing sounds. The method used an empirical data analysis approach of the Hilbert-Huang transform (HHT) to construct an instantaneous energy envelope and segment the cardiac cycle by detecting S1 and S2 sounds using the K-means algorithm. The time-domain HRV analysis for the short-term recordings of 10 subjects demonstrated a close agreement with the reference ECG signal. The instantaneous heart rate (IHR) comparisons yielded an accuracy of 95.78% and 92.35% for S1 and S2 sounds respectively. The experimental results showed that the proposed algorithm can provide an accurate HRV analysis for the cardiac signals recorded at the neck.