Morbid obesity influences the nocturnal electrocardiogram wave and interval durations among suspected sleep apnea patients.

BACKGROUND: Obesity is a global issue with a major impact on cardiovascular health. This study explores how obesity influences nocturnal cardiac electrophysiology in suspected obstructive sleep apnea (OSA) patients. METHODS: We randomly selected 12 patients from each of the five World Health Organization body mass index (BMI) classifications groups (ntotal = 60) while keeping the group's age and sex matched. We evaluated 1965 nocturnal electrocardiography (ECG) samples (10 s) using modified lead II recorded during normal saturation conditions. R-wave peaks were detected and confirmed using dedicated software, with the exclusion of ventricular extrasystoles and artifacts. The duration of waves and intervals was manually marked. The average electric potential graphs were computed for each segment. Thresholds for abnormal ECG waveforms were P-wave > 120 ms, PQ interval > 200 ms, QRS complex > 120 ms for, and QTc > 440 ms. RESULTS: Obesity was significantly (p < .05) associated with prolonged conduction times. Compared to the normal weight (18.5 ≤ BMI < 25) group, the morbidly obese patients (BMI ≥ 40) had a significantly longer P-wave duration (101.7 vs. 117.2 ms), PQ interval (175.8 vs. 198.0 ms), QRS interval (89.9 vs. 97.7 ms), and QTc interval (402.8 vs. 421.2 ms). We further examined ECG waveform prolongations related to BMI. Compared to other patient groups, the morbidly obese patients had the highest number of ECG segments with PQ interval (44% of the ECG samples), QRS duration (14%), and QTc duration (20%) above the normal limits. CONCLUSIONS: Morbid obesity predisposes patients to prolongation of cardiac conduction times. This might increase the risk of arrhythmias, stroke, and even sudden cardiac death.

QTc prolongation is associated with severe desaturations in stroke patients with sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is associated with vascular diseases from which stroke and sudden cardiac death are the most significant ones. It is known that disturbances of the autonomic nervous system and electrocardiographic changes are seen in patients with a previous cerebrovascular event. However, the pathophysiological cascade between breathing cessations, autonomic regulation, and cardiovascular events is not fully understood. METHODS: We aimed to investigate the acute effect of desaturation on repolarisation in OSA patients with a previous stroke. We retrospectively analysed heart-rate corrected QT (QTc) intervals before, within, and after 975 desaturations in OSA patients with a stroke history and at least moderate sleep apnea (apnea-hypopnea index ≥ 15 events/h, n = 18). For the control population (n = 18), QTc intervals related to 1070 desaturation were analysed. Desaturations were assigned to groups according to their length and duration. Groupwise comparisons and regression analyses were further executed to investigate the influence of desaturation features on repolarization. RESULTS: In the stroke population the QTc prolonged at least 11 ms during 27.1% of desaturations, and over 20 ms during 12.2% of desaturations. QTc was significantly prolonged during longer (> 30 s, p < 0.04) and deeper (> 7%, p < 0.03) desaturations. Less severe desaturations didn't influence QTc. In median, QTc prolonged 7.5 ms during > 45 s desaturations and 7.4 ms during > 9% deep desaturations. In the control population, QTc prolongation was observed but to a significantly lesser extent than in stroke patients. In addition, desaturation duration was found to be an independent predictor of QTc prolongation (ß = 0.08, p < 0.001) among all study patients. CONCLUSIONS: We demonstrated that longer (> 30 s) and deeper (> 7%) desaturations prolong QTc in patients with stroke history. A significant proportion of desaturations produced clinically relevant QTc prolongation. As it is known that a long QTc interval is associated with lethal arrhythmias, this finding might in part explain the pathophysiological sequelae of cardiovascular mortality in OSA patients with a history of stroke.

Autonomic responses to tooth clenching and handgrip test.

OBJECTIVE: The aim was to study the differences in autonomic nervous system activation between maximal tooth clenching task and handgrip test during and after the tasks. Also, the possible activation of trigeminocardiac reflex during the clenching task was explored. MATERIAL AND METHODS: We compared autonomic responses to maximal tooth clenching and handgrip in 28 participants. Responses in heart rate variability, heart rate, and blood pressure were evaluated before, during, and after tests. Although all study participants were considered healthy during recruitment, 14 of them showed painful temporomandibular disorders in the clinical examination, which was taken into account in the analyses. RESULTS: Handgrip and tooth clenching caused similar autonomic responses. However, tooth clenching seemed to activate the trigeminocardiac reflex shown as clenching-related vagal activation. The painful signs of temporomandibular disorders may interfere with the heart rate variability both at the baseline and during both tests causing significant variation in them. CONCLUSIONS: Both handgrip and tooth clenching affect the autonomic nervous system function. Tooth clenching differs from the handgrip due to trigeminocardiac reflex. Painful signs of temporomandibular disorders are interfering with the results of the tests and maybe underestimated in the studies of autonomic responses to both tasks.

Electrical and mechanical dyssynchrony in patients with right bundle branch block.

BACKGROUND: Though fairly benign reputation, the right bundle branch block (RBBB) can cause left ventricular mechanical dyssynchrony (LVMD). Still, the relationship between electrical disturbance and LVMD is partly unclear among these patients. METHODS: Thirty patients with RBBB and 60 matching controls were studied with vector electrocardiography and myocardial perfusion imaging phase analysis. RBBB group was divided into those with and those without LVMD. RESULTS: Prevalence of LVMD among RBBB patients was 50% and among controls 22%. Odds ratio (OR) for LVMD in patients with RBBB vs controls without RBBB was 3.6 (95% CI 1.4 to 9.3). Ejection fraction (EF), end-systolic volume, the angle between QRS and T vectors, and the QRS angle in the sagittal plane were significantly different between RBBB patients with and without LVMD. The QRS duration was comparable in these groups. EF associated independently with LVMD, explaining 60% of its variation. A cut-off value of EF ≤ 55% detected LVMD in 100% specificity (sensitivity of 47%). CONCLUSION: Half of the patients with RBBB had LVMD. The OR for LVMD between RBBB and normal ECG was 3.6. It seems that EF, rather than electrical parameters, is the main determinant of LVMD. This information might be useful when evaluating indications for cardiac resynchronization therapy.

Relationships between electrical and mechanical dyssynchrony in patients with left bundle branch block and healthy controls.

BACKGROUND: Abnormal electrical activation may cause dyssynchronous left ventricular (LV) contraction. In this study, we characterized and analyzed electrical and mechanical dyssynchrony in patient with left bundle branch block (LBBB) and healthy controls. METHODS: Myocardial perfusion imaging (MPI) data from 994 patients were analyzed. Forty-three patient fulfilled criteria for LBBB and 24 for controls. Electrical activation was characterized with vector electrocardiography (VECG) and LV function including mechanical dyssynchrony with ECG-gated MPI phase analysis. RESULTS: QRS duration (QRSd; r = 0.69, P < .001) and a few other VECG parameters correlated significantly with phase bandwidth (phaseBW) representing mechanical dyssynchrony. End-diastolic volume (EDV; r = 0.59, P < .001), ejection fraction and end-systolic volume correlated also with phaseBW. QRSd (ß = 0.47, P < .001) and EDV (ß = 0.36, P = .001) were independently associated with phaseBW explaining 55% of its variation. Sixty percent of patients with LBBB had significant mechanical dyssynchrony. Those patients had wider QRSd (159 vs 147 ms, P = .013) and larger EDV (144 vs 94 mL, P = .008) than those with synchronous LV contraction. Cut-off values for mechanical dyssynchrony seen in patients with LBBB were QRSd ≥ 165 ms and EDV ≥ 109 mL. CONCLUSIONS: Despite obvious conduction abnormality, LBBB is not always accompanied by mechanical dyssynchrony. QRSd and EDV explained 55% of variation seen in phaseBW. These two parameters were statistically different between LBBB cases with and without mechanical dyssynchrony.

Global electrical heterogeneity as a predictor of cardiovascular mortality in men and women.

Aims: The aim of this study was to investigate the contribution of depolarization and repolarization abnormalities, specially abnormalities in global electrical heterogeneity of heart in cardiovascular disease (CVD) and all-cause mortality. Methods and results: Eight hundred and forty men and 911 women, average age of 63 years participated in this study with average follow-up was 14 years. Six electrocardiogram/vector electrocardiogram (ECG/VECG) markers QRS-duration, QTc-interval, QRST-angle, sum of absolute QRST integral (SAI QRST), T-wave roundness, and TV1-amplitude were estimated from VECG measurements. Hazard ratios (HRs) for CVD events (164 deaths) and all-cause mortality (383 deaths) for ECG parameters were calculated. Electrocardiogram or vector electrocardiogram parameter models adjusted for risk clinical factors showed that strongest predictors for CVD mortality were QRST-angle (HR 3.44, 95% confidence interval 2.12-5.36), QTc-interval (2.72, 1.73-4.29), and T-wave roundness (2.09, 1.26-3.46) among men. The strongest ECG/VECG parameters for CVD death were QRST-angle (2.47, 1.37-4.45), SAI QRST (2.37, 1.23-4.6), and QTc-interval (2.15, 1.16-4.01) among female participants. Multivariable adjusted models revealed that strongest independent ECG predictors for CVD death were QRST-angle, QTc-interval, resting heart rate, and T-roundness for men, QRST-angle and SAI QRST for women. QRST-angle, QTc-interval, resting heart rate, and T-roundness were associated with all-cause mortality in male population, although none of the ECG/VECG parameters predicted all-cause mortality among women. Conclusion: Characteristics of global electrical heterogeneity QRST-angle and QTc-interval in men and QRST-angle and SAI QRST among females were strong and independent risk markers for cardiovascular mortality. These parameters provide new additional ECG tools for cardiovascular risk stratification.

Autonomic responses to tooth clenching in migraineurs-augmented trigeminocardiac reflex?

BACKGROUND: Systemic autonomic changes are well known in migraineurs. Also, masticatory disorders are reported to be associated with migraine. However, if those phenomena are interrelated, and how, is unclear. Moreover, the knowledge on the autonomic responses to masticatory stimuli in migraineurs is limited. OBJECTIVE: To investigate tooth clenching-related cardiac autonomic regulation in migraineurs. METHODS: We compared maximal tooth clenching-induced systemic autonomic responses, indicated by heart rate variability and blood pressure changes, in headache-free migraineurs (n = 17) and control subjects (n = 22). RESULTS: Levels of high-frequency power, reflecting vagal activity, were lower in migraineurs at baseline but increased after tooth clenching whereas in controls they returned to baseline (P < 0.05, mixed model analysis). In multivariate regression model, the presence of migraine predicted the baseline levels of low- and high-frequency power and sympathovagal balance, and the post-test increase in high-frequency power, with the attack frequency and side of headache as the modifiers of the measured changes in migraineurs. The painful signs of temporomandibular disorders, found in clinical oral examination, enhanced both maximal changes in RR intervals and post-test vagal responses to tooth clenching only in migraineurs. CONCLUSION: The enhanced post-clenching vagal activation may represent a marker of the augmented trigeminocardiac reflex to stimulation of trigeminal area, sensitised in migraineurs. Our results support an involvement of autonomic mechanisms in migraine pathophysiology and are interesting in terms of interactions between migraine and masticatory disorders, elucidating one potential way how masticatory disorders may aggravate migraine.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation.

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Inter-sleep stage variations in corrected QT interval differ between obstructive sleep apnea patients with and without stroke history.

Obstructive sleep apnea (OSA) is related to the progression of cardiovascular diseases (CVD); it is an independent risk factor for stroke and is also prevalent post-stroke. Furthermore, heart rate corrected QT (QTc) is an important predictor of the risk of arrhythmia and CVD. Thus, we aimed to investigate QTc interval variations in different sleep stages in OSA patients and whether nocturnal QTc intervals differ between OSA patients with and without stroke history. 18 OSA patients (apnea-hypopnea index (AHI)≥15) with previously diagnosed stroke and 18 OSA patients (AHI≥15) without stroke history were studied. Subjects underwent full polysomnography including an electrocardiogram measured by modified lead II configuration. RR, QT, and QTc intervals were calculated in all sleep stages. Regression analysis was utilized to investigate possible confounding effects of sleep stages and stroke history on QTc intervals. Compared to patients without previous stroke history, QTc intervals were significantly higher (ß = 34, p<0.01) in patients with stroke history independent of age, sex, body mass index, and OSA severity. N3 sleep (ß = 5.8, p<0.01) and REM sleep (ß = 2.8, p<0.01) increased QTc intervals in both patient groups. In addition, QTc intervals increased progressively (p<0.05) towards deeper sleep in both groups; however, the magnitude of changes compared to the wake stage was significantly higher (p<0.05) in patients with stroke history. The findings of this study indicate that especially in deeper sleep, OSA patients with a previous stroke have an elevated risk for QTc prolongation further increasing the risk for ventricular arrhythmogenicity and sudden cardiac death.

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study.

BACKGROUND: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. OBJECTIVE: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. METHODS: Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. RESULTS: Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. CONCLUSIONS: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. TRIAL REGISTRATION: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335.

Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is the major cause of stroke since approximately 25% of all strokes are of cardioembolic-origin. The detection and diagnosis of AF are often challenging due to the asymptomatic and intermittent nature of AF. HYPOTHESIS: A wearable electrocardiogram (ECG)-device could increase the likelihood of AF detection. The aim of this study was to evaluate the feasibility and reliability of a novel, consumer-grade, single-lead ECG recording device (Necklace-ECG) for screening, identifying and diagnosing of AF both by a cardiologist and automated AF-detection algorithms. METHODS: A thirty-second ECG was recorded with the Necklace-ECG device from two positions; between the palms (palm) and between the palm and the chest (chest). Simultaneously registered 3-lead ECGs (Holter) served as a golden standard for the final rhythm diagnosis. Two cardiologists interpreted independently in a blinded fashion the Necklace-ECG recordings from 145 patients (66 AF and 79 sinus rhythm, SR). In addition, the Necklace-ECG recordings were analyzed with an automatic AF detection algorithm. RESULTS: Two cardiologists diagnosed the correct rhythm of the interpretable Necklace-ECG with a mean sensitivity of 97.2% and 99.1% (palm and chest, respectively) and specificity of 100% and 98.5%. The automatic arrhythmia algorithm detected the correct rhythm with a sensitivity of 94.7% and 98.3% (palm and chest) and specificity of 100% of the interpretable measurements. CONCLUSIONS: The novel Necklace-ECG device is able to detect AF with high sensitivity and specificity as evaluated both by cardiologists and an automated AF-detection algorithm. Thus, the wearable Necklace-ECG is a new, promising method for AF screening. CLINICAL TRIAL REGISTRATION: Study was registered in the ClinicalTrials.gov database (NCT03753139).

Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study.

BACKGROUND: Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF's asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. OBJECTIVE: We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. METHODS: Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). RESULTS: The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient's daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). CONCLUSIONS: A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335.

Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection.

Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p < 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF. Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335). Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home. Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data's quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.

A robust algorithm for heart rate variability time series artefact correction using novel beat classification.

Purpose: Heart rate variability is a commonly used measurement to evaluate functioning of autonomic nervous system, psychophysiological stress, and exercise intensity and recovery. HRV measurements contain artefacts such as extra, missed or misaligned beat detections, which can produce significant distortion on HRV parameters. In this paper, a robust automatic method for artefact detection from HRV time series is proposed. Methods: The proposed detection method is based on time-varying thresholds estimated from distribution of successive RR-interval differences combined with a novel beat classification scheme. The method is validated using simulated extra, missed and misaligned beat detections as well as real artefacts such as atrial and ventricular ectopic beats. Results: The sensitivity of the algorithm to detect simulated missed/extra beats was 100%. The sensitivity to detect real atrial and ventricular ectopic beats was 96.96%, the corresponding specificity being 99.94%. The mean error in HRV parameters after correction was <2% for missed and extra beats as well as for misaligned beats generated with large displacement factors. Misaligned beats with smallest displacement factor were the most difficult to detect and resulted in largest HRV parameter errors after correction, largest errors being <8%. Conclusions: The HRV artefact correction algorithm presented in this study provided comparable specificity and better sensitivity to detect ectopic beats as compared to state-of-the-art algorithms. The proposed algorithm detects abnormal beats with high accuracy, is relatively easy to implement, and secures reliable HRV analysis by reducing the effect of possible artefacts to tolerable level.

Effectiveness of the Chest Strap Electrocardiogram to Detect Atrial Fibrillation.

Atrial fibrillation (AF) is a significant cause of cardioembolic strokes. AF is often symptomless and intermittent, making its detection challenging. The aim of this study was to assess the possibility to use a chest strap (Suunto Movesense) to detect AF both by cardiologists and automated algorithms. A single channel electrocardiogram (ECG) from a chest strap of 220 patients (107 AF and 111 sinus rhythm SR with 2 inconclusive rhythms) were analyzed by 2 cardiologists (Doc1 and Doc2) and 2 different algorithms (COSEn and AFEvidence). A 3-lead Holter served as the gold standard ECG for rhythm analysis. Both cardiologists evaluated the quality of the chest strap ECG to be superior to the quality of the Holter ECG; p <0.05/p <0.001 (Doc1/Doc 2). Accurate automated algorithm-based AF detection was achieved with sensitivity of 95.3%/96.3% and specificity of 95.5/98.2% with 2 AF detection algorithms from chest strap and 93.5%/97.2% and 98.2%/95.5% from Holter, respectively. P waves were detectable in 93.7% (Doc1) and 94.6% (Doc2) of the cases from the chest strap ECG with sinus rhythm and 98.2% (Doc1) and 95.5% (Doc2) from the Holter (p = n.s). In conclusion, the ECGs from both methods enabled AF detection by a cardiologist and by automated algorithms. Both methods studied enabled P-wave detection in sinus rhythm.

Tooth Clenching Induces Abnormal Cerebrovascular Responses in Migraineurs.

Prevalence of masticatory parafunctions, such as tooth clenching and grinding, is higher among migraineurs than non-migraineurs, and masticatory dysfunctions may aggravate migraine. Migraine predisposes to cerebrovascular disturbances, possibly due to impaired autonomic vasoregulation, and sensitization of the trigeminovascular system. The relationships between clenching, migraine, and cerebral circulation are poorly understood. We used Near-Infrared Spectroscopy to investigate bilateral relative oxy- (%Δ[O2Hb]), deoxy- (%Δ[HHb]), and total (%Δ[tHb]) hemoglobin concentration changes in prefrontal cortex induced by maximal tooth clenching in twelve headache-free migraineurs and fourteen control subjects. From the start of the test, migraineurs showed a greater relative increase in right-side %Δ[HHb] than controls, who showed varying reactions, and right-side increase in %Δ[tHb] was also greater in migraineurs (p < 0.001 and p < 0.05, respectively, time-group interactions, Linear mixed models). With multivariate regression model, migraine predicted the magnitude of maximal blood pressure increases, associated in migraineurs with mood scores and an intensity of both headache and painful signs of temporomandibular disorders (pTMD). Although changes in circulatory parameters predicted maximal NIRS responses, the between-group differences in the right-side NIRS findings remained significant after adjusting them for systolic blood pressure and heart rate. A family history of migraine, reported by all migraineurs and four controls, also predicted maximal increases in both %Δ[HHb] and %Δ[tHb]. Presence of pTMD, revealed in clinical oral examination in eight migraineurs and eight controls, was related to maximal %Δ[HHb] increase only in controls. To conclude, the greater prefrontal right-side increases in cerebral %Δ[HHb] and %Δ[tHb] may reflect disturbance of the tooth clenching-related cerebral (de)oxygenation based on impaired reactivity and abnormal microcirculation processes in migraineurs. This finding may have an impact in migraine pathophysiology and help to explain the deleterious effect of masticatory dysfunctions in migraine patients. However, the role of tooth clenching as a migraine trigger calls for further studies.

Heart rate variability evaluation of Emfit sleep mattress breathing categories in NREM sleep.

OBJECTIVE: Heart rate variability (HRV) analysis of obstructive sleep apnea patients reveals an increase in sympathetic activity. Sleep disordered breathing (SDB) can be also assessed with sleep mattress sensors, as the Emfit sensor, by dividing the signal into different breathing categories. In addition to normal breathing (NB) and periodic apneas/hypopneas (POB), the sleep mattress unveils a breathing category consisting of sustained partial obstruction (increased respiratory resistance, IRR). The aim of our study was to evaluate HRV during these three breathing categories in NREM sleep. METHODS: 53 patients with suspected SDB underwent an overnight polysomnography with an Emfit mattress. The Emfit signal was scored in 3-min epochs according to the established rules. The NB, POB, and IRR epochs were combined to as long NB, POB and IRR periods as possible and HRV was calculated from at least 6-min epochs. RESULTS: The meanHR did not differ between the breathing categories. HRV parameters revealed an increase in sympathetic activity during POB. The mean LF/HF ratio was highest during POB (3.0) and lowest during IRR (1.3). During NB it was 1.7 (all p-values ⩽ 0.001). Interestingly sympathetic activity decreased and parasympathetic activity increased during IRR as compared to NB (the mean HF power was 1113.8 ms(2) during IRR and 928.4 ms(2) during NB). CONCLUSIONS: The HRV findings during POB resembled HRV results of sleep apnea patients but during sustained prolonged partial obstruction a shift towards parasympathetic activity was achieved. SIGNIFICANCE: The findings encourage the use of sleep mattresses in SDB diagnostics. In addition the findings suggest that sustained partial obstruction represents its own SDB entity.

Mouse ECG findings in aging, with conduction system affecting drugs and in cardiac pathologies: Development and validation of ECG analysis algorithm in mice.

Mouse models are extremely important in studying cardiac pathologies and related electrophysiology, but very few mouse ECG analysis programs are readily available. Therefore, a mouse ECG analysis algorithm was developed and validated. Surface ECG (lead II) was acquired during transthoracic echocardiography from C57Bl/6J mice under isoflurane anesthesia. The effect of aging was studied in young (2-3 months), middle-aged (14 months) and old (20-24 months) mice. The ECG changes associated with pharmacological interventions and common cardiac pathologies, that is, acute myocardial infarction (AMI) and progressive left ventricular hypertrophy (LVH), were studied. The ECG raw data were analyzed with an in-house ECG analysis program, modified specially for mouse ECG. Aging led to increases in P-wave duration, atrioventricular conduction time (PQ interval), and intraventricular conduction time (QRS complex width), while the R-wave amplitude decreased. In addition, the prevalence of arrhythmias increased during aging. Anticholinergic atropine shortened PQ time, and beta blocker metoprolol and calcium-channel blocker verapamil increased PQ interval and decreased heart rate. The ECG changes after AMI included early JT elevation, development of Q waves, decreased R-wave amplitude, and later changes in JT/T segment. In progressive LVH model, QRS complex width was increased at 2 and especially 4 weeks timepoint, and also repolarization abnormalities were seen. Aging, drugs, AMI, and LVH led to similar ECG changes in mice as seen in humans, which could be reliably detected with this new algorithm. The developed method will be very useful for studies on cardiovascular diseases in mice.