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Objective - Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assessment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach - We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximum a-posterior estimation and a Markov decision process to approach an optimal solution. Main Results - The maximum a-posterior estimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heart rate measurement. Performance was held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance - The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.

OBJECTIVE: Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life.APPROACH: The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement.MAIN RESULTS: CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa = 0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa = 0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%-57.6% depending on the movement threshold compared to 92.1% of ECG.SIGNIFICANCE: The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monito

Background-Long-term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation (AF). This study examined the accuracy of a novel approach for AF detection using photo-plethysmography signals measured from a wrist-based wearable device. Methods and Results-ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion (ECV) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings (HOL). Photo-plethysmography and acceleration data were collected at the wrist and processed to determine the inter-pulse interval and discard inter-pulse intervals in presence of motion artifacts. A Markov model was deployed to assess the probability of AF given irregular pattern in inter-pulse interval sequences. The AF detection algorithm was evaluated against clinical rhythm annotations of AF based on ECG interpretation. Photo-plethysmography recordings from apparently healthy volunteers (n=120) were used to establish the false positive AF detection rate of the algorithm. A total of 42 and 855 hours (AF: 21 and 323 hours) of photo-plethysmography data were recorded in the ECV and HOL cohorts, respectively. AF was detected with >96% accuracy (ECV, sensitivity=97%; HOL, sensitivity=93%; both with specificity=100%). Because of motion artifacts, the algorithm did not provide AF classification for 44±16% of the monitoring period in the HOL group. In healthy controls, the algorithm demonstrated a <0.2% false positive AF detection rate. Conclusions-A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

Atrial fibrillation (AF) is the most commonly experienced sustained arrhythmia, and it increases risks of stroke and congestive heart failure. Unobtrusive wearable solutions with photoplethysmography (PPG) have been proposed for AF detection and the performance has been mainly evaluated for short-term measurements in controlled measurement settings. In this study, we evaluate the predictive value of features from PPG for AF detection under both hospital and free-living conditions. PPG from the wrist was measured from 18 patients before and after cardioversion and from 16 patients (4 with 100% AF) for 24 hours. Single-lead ECG and 24-hour Holter were used respectively as gold standards. Six PPG-based inter-beat interval (IBI) variability and irregularity features were computed in three different sliding time windows. Thresholds for AF classification for every individual feature were determined with the data from the hospital conditions and tested with the measurements from free-living conditions. Overall, the best classification results were obtained by using a 120-s window, pNN40 resulting as the best feature. On average, the sensitivity was higher in the hospital conditions (92.3% vs. 71.6%) and the specificity higher in the free-living conditions (60.7% vs. 84.9%). In conclusion, testing the classification perfomance in free-living conditions is essential to properly evaluate AF detection models.

Atrial fibrillation (AF) is a pathological cardiac condition leading to increased risk for embolic stroke. Screening for AF is challenging due to the paroxysmal and asymptomatic nature of the condition. We aimed to investigate whether an unobtrusive wrist-wearable device equipped with a photo-plethysmographic (PPG) and acceleration sensor could detect AF. Sixteen patients with suspected AF were monitored for 24 hours in an outpatient setting using a Holter ECG. Simultaneously, PPG and acceleration data were collected at the wrist. PPG data was processed to determine the timing of heartbeats and inter-beat-interval (IBI). Wrist acceleration and PPG morphology were used to discard IBIs in presence of motion artefacts. An ECG validated first-order Markov model was used to assess the probability of irregular rhythm due to AF using PPG-derived IBIs. The AF detection algorithm was compared with clinical adjudications of AF episodes after review of the ECG records. AF detection was achieved with 97 ± 2% sensitivity and 99 ± 3% specificity. Due to motion artefacts, the algorithm did not provide AF classification for an average of 36 ± 9% of the 24 hours monitoring. We concluded that a wrist-wearable device equipped with a PPG and acceleration sensor can accurately detect rhythm irregularities caused by AF in daily life.