A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

Abstract

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches.

An Electrocardiograph based Arrythmia Detection System

“…The morphological operation is performed here using dilation and erosion operation applied on the input ECG signal. If x(t) and y(t) be the subsets of an Euclidean space E, where x(t) is a one-dimensional signal, and y(t) is the predefined structuring element then [1] dilation (x, y) = max{x(t-u) +y(u)} erosion (x y) = min{x(t-u)-g(u)} The denoising of the ECG signal is performed based on the following algorithm. If (dilation > dc level) avg = erode + dilation else avg= erode-dilation.…”

“…Hence the ECG signals are used for diagnosing the healthy status of the heart. [1] ECG signals are divided into four important electrical events each representing the phases of a cardiac cycle. The four events are 'P' wave, 'QRS' complex.…”

Context-Awareness for Mobile Sensing: A Survey and Future Directions

“…The studies in [120]- [122] are carried out in order to create a smart home environment for treatment procedures of patients (e.g., having cardiac problem [123], or diabetics) based on collecting data through different wearable physiological sensors (e.g., body temperature, heart rate, blood pressure, blood oxygen values, respiration level, and ECGs) and also reporting feedbacks remotely to the healthcare givers. The wearable sensors (including accelerometers, heart rate monitors and many others) have been also studied in [124]- [127] to recognize activity patterns for measuring fitness level, and discovering frequentness of body movement against obesity and weight 1553-877X (c) 2013 IEEE.…”

Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

“…Since these contaminants overlap the cardiac component in both the spatiotemporal and frequency domains, the extraction of weak cardiac components from the corrupted ECG signals and the preservation of the subtle features of the signals are fairly difficult. Indeed, ECG signal denoising is very important for improving the quality of ECG signals, thereby facilitating subsequent signal processing and analysis tasks such as ECG watermarking, compression [1], feature extraction and classification [2,3]. …”