Autoencoder-Based Neural Network Model for Anomaly Detection in Wireless Body Area Networks

• Published in: IoT Vol. 5; no. 4; pp. 852 - 870
• Main Author: Rassam, Murad A.
• Format: Journal Article
• Language: English
• Published: Montreal MDPI AG 01.12.2024
• Subjects: Accuracy; anomaly detection; autoencoders; Big Data; Blood pressure; Data compression; Datasets; Electrocardiography; Energy consumption; Intensive care; Machine learning; Medical personnel; Neural networks; Older people; Patients; Physiology; real-world dataset; Sensors; wireless body area networks
• ISSN: 2624-831X; 2624-831X
• DOI: 10.3390/iot5040039

In medical healthcare services, Wireless Body Area Networks (WBANs) are enabler tools for tracking healthcare conditions by monitoring some critical vital signs of the human body. Healthcare providers and consultants use such collected data to assess the status of patients in intensive care units (ICU) at hospitals or elderly care facilities. However, the collected data are subject to anomalies caused by faulty sensor readings, malicious attacks, or severe health degradation situations that healthcare professionals should investigate further. As a result, anomaly detection plays a crucial role in maintaining data quality across various real-world applications, including healthcare, where it is vital for the early detection of abnormal health conditions. Numerous techniques for anomaly detection have been proposed in the literature, employing methods like statistical analysis and machine learning to identify anomalies in WBANs. However, the lack of normal datasets makes training supervised machine learning models difficult, highlighting the need for unsupervised approaches. In this paper, a novel, efficient, and effective unsupervised anomaly detection model for WBANs is developed using the autoencoder convolutional neural network (CNN) technique. Due to their ability to reconstruct data in a completely unsupervised manner using reconstruction error, autoencoders hold great potential. Real-world physiological data from the PhysioNet dataset evaluated the suggested model’s performance. The experimental findings demonstrate the model’s efficacy, which provides high detection accuracy, as reported F1-Score is 0.96 with a batch size of 256 along with a mean squared logarithmic error (MSLE) below 0.002. Compared to existing unsupervised models, the proposed model outperforms them in effectiveness and efficiency.