Exploration of ECG-Based Real-Time Arrhythmia Detection: A Systematic Literature Review

Rizqyawan, Muhammad Ilham; Nuryatno, Edi Triono; Fakhrurroja, Hanif; Munandar, Aris; Wibowo, Jony Winaryo; Kusumandari, Dwi Esti; Simbolon, Artha Ivonita; Setiawan, Dika; Amri, M. Faizal; Nadiya, Ulfah; Salim, Taufik Ibnu

doi:10.1109/icadeis56544.2022.10037399

Cited by 2 publications

(1 citation statement)

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“…Feature engineering involves the extraction of specific features from the ECG waveform. Some studies use raw signals due to the increasing popularity of deep learning (DL) methods [18], while others identify fiducial points such as QRS-complex, ST-segment, R-peak, and P-and T-waves. Many traditional signal processing approaches have been proposed for feature engineering, such as wavelet transform and its variations, methods based on time domain and mathematical morphology, and derivative-based techniques, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications

Manokhin,

Chollet,

Desgreys

2024

Sensors

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Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications.

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Section: Introductionmentioning

confidence: 99%