Current wavelet thresholding methods for cardiogram signals captured by flexible wearable
sensors face a challenge in achieving both accurate thresholding and real-time signal …

Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are
not conducive to heart rate variability (HRV) analysis. The two types of noise most often …

Surface electromyography (sEMG), a non-invasive technique, offers the ability to identify
insights into the activities of muscles in the form of electrical pulses. During the process of …

The removal of unwanted noise from electrocardiogram (ECG) recordings is a difficult
procedure in biomedical signal analysis. It alters the signal and affects the accurate …

Surface electromyography (sEMG) signal quality decreases when it is contaminated by
different types of artifacts. Detection and removal of the contaminants from sEMG signals …

Plant sensor data contain errors that can hamper process analysis and decision-making.
Those dataset are not used to their full potential due to the complexity of their processing …

The classification of ECG signals is a critical process because it guides the diagnosis of the
proper treatment process for the patient. However, any form of disturbance with ECG signals …

The primary obstacle in using wearable devices to record dynamic Electrocardiogram (ECG)
signal for more efficient analysis of cardiac problems is noise interference, which can cause …

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing
technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are …

De-noising of ECG signal is very much necessary to monitor the heart health and to
diagnose disease. This paper presents a real time de-noising system that efficiently wiped …