Abstract-This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG)wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multilead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline single-lead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time, and memory usage. Interestingly, RMS-based multilead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for long-lasting wearable ECG monitoring systems.Index Terms-Ambulatory electrocardiogram, delineation, digital wavelet transform, energy-constrained systems, multilead, wireless sensor node.

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.

Abstract-This work presents a new modular and lowcomplexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform realtime delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in run time to a wide range of modes and sampling rates, from a Ultra-low power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete High-accuracy delineation mode in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in case of arrhythmia. The delineation algorithm has been adjusted using the QT database, providing very high sensitivity and positive predictivity, and validated with the MIT database. The errors in the delineation of all the fiducial points are below the tolerances given by the Common Standards for Electrocardiography (CSE) committee in the High-accuracy mode, except for the P wave onset, for which the algorithm is above the agreed tolerances by only a fraction of the sample duration. The computational load for the ultra-low-power 8-MHz TI MSP430 series microcontroller ranges from 0.2 to 8.5% according to the mode used.

Abstract-The analysis of the electrocardiogram (ECG) is widely used for diagnosing many cardiac diseases. Since most of the clinically useful information in the ECG is found in characteristic wave peaks and boundaries, a significant amount of research effort has been devoted to the development of accurate and robust algorithms for automatic detection of the major ECG characteristic waves (i.e., the QRS complex, P and T waves), socalled ECG wave delineation.One of the most salient ECG wave delineation algorithms is based on the wavelet transform (WT). This work is dedicated to the sensible optimization and porting of this WT-based ECG wave delineator to an actual wearable embedded sensor platform with limited processing and storage resources. The porting was successful and the implementation was extensively validated using a standard manually annotated database. Interestingly, our results show that, despite the limitations of the embedded sensor platform, careful optimization allows to achieve comparable or even better delineation results than the original offline algorithm.