Modulation spectrum analysis for noisy
\nelectrocardiogram signal processing and applications.

Advances in wearable electrocardiogram (ECG) monitoring devices have allowed for new cardiovascular \napplications to emerge beyond diagnostics, such as stress and fatigue detection, \nathletic performance assessment, sleep disorder characterization, mood recognition, activity \nsurveillance, biometrics, and fitness tracking, to name a few. Such devices, however, are \nprone to artifacts, particularly due to movement, thus hampering heart rate and heart rate \nvariability measurement and posing a serious threat to cardiac monitoring applications. To \naddress these issues, this thesis proposes the use of a spectro-temporal signal representation \ncalled “modulation spectrum”, which is shown to accurately separate cardiac and noise components \nfrom the ECG signals, thus opening doors for noise-robust ECG signal processing \ntools and applications. \nFirst, an innovative ECG quality index based on the modulation spectral signal representation \nis proposed. The representation quantifies the rate-of-change of ECG spectral \ncomponents, which are shown to be different from the rate-of-change of typical ECG noise \nsources. As such, a signal-to-noise ratio (SNR) like metric is proposed, termed modulation \nspectral based quality index (MS-QI). Unlike existing quality metrics, MS-QI does not rely \non machine learning algorithms, can be performed on single-lead ECGs, and was shown to \nperform accurately with synthetic ECGs, as well as ECGs recorded in real-world environments. \nBased on insights obtained from the MS-QI metric, a new adaptive ECG enhancement \nalgorithm is then proposed based on the principle of bandpass filtering in the modulation \nspectral domain. The algorithm was tested on synthetic and recorded (extremely noisy) \nECG databases. Experimental results show the proposed algorithm outperforming a stateof- \nthe-art wavelet-based enhancement algorithm in terms of heart rate (HR) error percentage \nmeasurement, signal-to-noise ratio (SNR) improvement, and ECG kurtosis; the latter is a \nwidely-used ECG quality metric. These findings suggest that the proposed algorithm can be \nused to enhance the quality of wearable ECG monitors even in extreme conditions, thus it \ncan play a key role in athletic peak performance training/monitoring. \nMoreover, wearable ECG monitoring applications are burgeoning and typically rely on \nestimates of heart rate variability (HRV). Such applications require small computational footprint \nand cannot rely on enhancement and HRV analysis, thus a stand-alone HRV metric is \nneeded. HRV indices have been proposed based on time- and frequency-domain analyses of \nthe ECG, as well as via non-linear approaches. These methods, however, are very sensitive \nto ECG artefacts, thus limiting the number of applications involving noisy ECGs (e.g., athletic peak performance training). Typically, ECG enhancement is performed prior to HRV \ncomputation to overcome this limitation. Existing enhancement algorithms, however, are \nnot accurate in very noisy scenarios. Hence, an alternate approach is proposed based on \nthe modulation spectrum. By quantifying the rate-of-change of ECG spectral components \nover time, we show that heart rate estimates can be reliably obtained even in extremely noisy \nsignals, thus bypassing the need for ECG enhancement. The so-called MD-HRV (modulation \ndomain HRV) is tested on synthetic and recorded noisy ECG signals and shown to outperform \nseveral benchmark HRV metrics computed post-enhancement. These findings suggest that \nthe proposed MD-HRV metric is well-suited for ambulant cardiac monitoring applications, \nparticularly those involving intense movement. \nFinally, a quality-aware ECG monitoring application is presented based on the proposed \nMS-QI. Wearable ECG devices are increasingly being used in telehealth applications, particularly \nfor patient monitoring applications. Representative devices include watches, chest \nstraps, and even smart clothing via textile ECG sensors. Such lower-cost sensors, however, \nare extremely sensitive to movement, thus pose a serious threat to such ECG streaming applications. \nFor example, transmission bandwidth, battery life, and/or storage space can be \nspent with ECG segments that convey little cardiac information due to the high levels of \nnoise present. Moreover, noisy signals may cause false alarms in automated patient monitoring \nsystems, thus increasing the burden on medical personnel. Here, by employing the \nMS-QI to discriminate usable from non-usable ECG segments, a quality-aware storage protocol \nwas implemented where storage of cardiac parameters was only performed on the usable \nsegments. When tested with a smart shirt under three conditions, namely sitting, walking \nand running, the proposed quality-aware application resulted in storage savings of 65%.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.