Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Abstract

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

“…The need to reduce the energy consumption of wearable devices has given rise to a plethora of studies. In [25], a realtime classification scheme for automatic detection of abnormal heartbeats targeting embedded and resource-constrained wearables has been proposed. This scheme also incorporates an advanced digital signal processing block that is activated just when abnormal beats are detected, which considerably decreases the computational requirements and the energy consumption.…”

Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices

“…The need to reduce the energy consumption of wearable devices has given rise to a plethora of studies. In [9], a real-time classification scheme for automatic detection of abnormal heartbeats targeting embedded and resource-constrained Wireless Body Sensor Nodes (WBSNs) has been proposed. This scheme also incorporates an advanced digital signal processing block that is activated just when abnormal beats are detected, which considerably decreases the computational requirements and the energy consumption.…”

Energy vs. Reliability Trade-offs Exploration in Biomedical Ultra-Low Power Devices

“…: low-pass or high-pass filtering) to a set of biosignal samples [9]. At the lower level, this operation consists of a series of matrix multiplication op-…”

“…Finally, some applications, such as the Heartbeat Classifier [9] (based on Wavelet Delineation + CS), produce statistical or qualitative results; After delineation, heartbeats are sorted out according to different classes of morphologies to detect patients' pathologies, and this task usually requires finetuning with human feedback to adjust the margin inherent to the classification algorithm. This classification is often performed visually by doctors and its precision depends on human interpretation with coarse-grained boundaries between classes.…”