Cardiac Ambulatory Monitoring: New Wireless Device Validated Against Conventional Holter Monitoring in a Case Series.

Background: Cardiac arrhythmias are very common but underdiagnosed due to their transient and asymptomatic nature. An optimization of arrhythmia detection would permit to better treat patients and could substantially reduce morbidity and mortality. The SmartCardia ScaAI wireless patch is a novel CE IIa approved, single-lead electrocardiographic (ECG) ambulatory monitor designed for cardiac arrhythmias detection. Hypothesis: The accuracy of the new SmartCardia wireless patch to detect arrhythmias is comparable to the conventional Holter monitoring. Methods: Patients referred for a suspicion of arrhythmia between February and March 2020 were included in the trial. Simultaneous ambulatory ECG were recorded using a conventional 24-h Holter and the SmartCardia. The primary endpoint was the detection of cardiac arrhythmias over the total wear time of the devices, defined as premature atrial contraction (PAC), supraventricular tachycardia ≥3 beats, premature ventricular contraction (PVC), and ventricular tachycardia ≥3 beats. Conduction abnormalities, pause ≥2 s and atrioventricular block (AVB), were also tracked. McNemar's test was used to compare the matched pairs of data from both devices. Results: A total of 40 patients were included in the trial. Over the total wear time, there was no significant difference between the devices for ventricular and supraventricular arrhythmias detection. Pauses and AVB were equally identified by the two devices in three patients. Conclusion: Over the total wear time, the SmartCardia device showed an accuracy to detect arrhythmia similar to the 24-h Holter monitoring: single-lead, adhesive-patch monitoring might become an interesting alternative to the conventional Holter monitoring.

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors.

Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things, it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA.

Energy-aware embedded classifier design for real-time emotion analysis.

Detection and classification of human emotions from multiple bio-signals has a wide variety of applications. Though electronic devices are available in the market today that acquire multiple body signals, the classification of human emotions in real-time, adapted to the tight energy budgets of wearable embedded systems is a big challenge. In this paper we present an embedded classifier for real-time emotion classification. We propose a system that operates at different energy budgeted modes, depending on the available energy, where each mode is constrained by an operating energy bound. The classifier has an offline training phase where feature selection is performed for each operating mode, with an energy-budget aware algorithm that we propose. Across the different operating modes, the classification accuracy ranges from 95% - 75% and 89% - 70% for arousal and valence respectively. The accuracy is traded off for less power consumption, which results in an increased battery life of up to 7.7 times (from 146.1 to 1126.9 hours).