QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy.

Design of Microsleep Alerting System of Pilot to Reduce Air Accidents

The pilot’s micro sleep often caused by fatigue and/or drowsiness receives increasing attention for the last few years, especially after it became evident that pilot’s micro sleep also one of the major factor causing serious aircraft accidents. The system comprises EOG, EEG and IR module. EEG measures the electrical activity of the brain called brain wave pattern through intrusive electrodes. EOG tapes the electrical potential of eyeball movements and the IR module senses the eye blink frequency. Then all these signals are applied to a robust signal processing unit and microcontroller. When an indicating feature corresponds to the micro sleep events are detected, the warning system is activated. This envisioned micro sleep alerting system would continuously monitor the alertness of the pilot and provides immediate warning signal, when micro sleep detected with high certainty.

Denoising of Fetal Phonocardiogram Signal by Wavelet Transformation

Auscultation is still one of the most basic analytical tools used to determine the fetal heart’s functional state as well as the first fetal well-being measure. It is called fetal phonocardiography (fPCG) in its modern form. The technique of fPCG is passive and can be used to track long-term. Robust signal processing techniques are required to denoise the signals in order to improve the diagnostic capabilities of fPCG. A linear filter is used to eliminate distortion and interference from the fPCG signals through conventional denoising techniques. This paper searched for optimal configuration of the wavelet based denoising system. Based on the experimental results, can be conclude that the signal should be decomposed on six levels. From this it can be seen that the lowest MSE (mean square error) value is the use of coiflets three with SURE threshold algorithm with hard threshold parameters.