An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning

Abstract

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics.

Design and Analysis of Area and Energy Efficient Reconfigurable Cryptographic Accelerator for Securing IoT Devices

“…The 65-nm ECG SoC in [ 20 ] is selected to integrate the cryptographic accelerator. For a smart sensor node, usually 20% of battery power [ 21 ] can be allocated by the ECG SoC, which consumes 0.254 J per day when the cryptographic accelerator is not integrated. The 65-nm design in [ 11 ] is selected for comparison, which has the highest energy efficiency among the three designs in [ 10 , 11 , 12 ].…”

Event-Driven ECG Sensor in Healthcare Devices for Data Transfer Optimization

“…Thus, Huffman compression is only applied on the residual data after online decompression and residual data request. As far as [56] is concerned, the proposed compression method detects turning angles larger than a threshold. In fact, the useful information for the diagnosis is in the rapid variations of the P wave, the QRS complex and the T wave.…”

“…Table 4 summarizes the lossy data compression state of the art. The obtained results are measured for one test ECG signal [56], two test ECG signals [58] or all test ECG signals [12] from MIT-BIH arrhythmia database. Thus, after the SAR ADC, the DWT-based compression methods provide the best trade-off between BCR and PRD comp [53].…”

Machine Learning for Healthcare Wearable Devices: The Big Picture

“…Transmission and reception of data are thought to consume more energy than sensing and logging data. Research in the area of reduction in the power consumption can be seen to go in different directions, developing special embedded hardware for running machine learning algorithms [ 116 , 117 ], reducing data to be transferred [ 118 – 120 ], compression [ 121 ] or scheduling of the data to be transferred [ 122 ], computational offloading [ 123 , 124 ], and developing self-powered wearable devices [ 125 , 126 ].…”