Chacko John Deepu

A Joint QRS Detection and Data Compression Scheme for Wearable Sensors

This paper presents a novel electrocardiogram (ECG) processing technique for joint data compression and QRS detection in a wireless wearable sensor. The proposed algorithm is aimed at lowering the average complexity per task by sharing the computational load among multiple essential signal-processing tasks needed for wearable devices. The compression algorithm, which is based on an adaptive linear data prediction scheme, achieves a lossless bit compression ratio of 2.286x. The QRS detection algorithm achieves a sensitivity (Se) of 99.64% and positive prediction (+P) of 99.81% when tested with the MIT/BIH Arrhythmia database. Lower overall complexity and good performance renders the proposed technique suitable for wearable/ambulatory ECG devices.

An ECG-on-Chip for Wearable Cardiac Monitoring Devices

This paper describes a highly integrated, low power chip solution for ECG signal processing in wearable devices. The chip contains an instrumentation amplifier with programmable gain, a band-pass filter, a 12-bit SAR ADC, a novel QRS detector, 8K on-chip SRAM, and relevant control circuitry and CPU interfaces. The analog front end circuits accurately senses and digitizes the raw ECG signal, which is then filtered to extract the QRS. The sampling frequency used is 256 Hz. ECG samples are buffered locally on an asynchronous FIFO and is read out using a faster clock, as and when it is required by the host CPU via an SPI interface. The chip was designed and implemented in 0.35µm standard CMOS process. The analog core operates at 1V while the digital circuits and SRAM operate at 3.3V. The chip total core area is 5.74 mm2 and consumes 9.6µW. Small size and low power consumption make this design suitable for usage in wearable heart monitoring devices.

A computationally efficient QRS detection algorithm for wearable ECG sensors

In this paper we present a novel Dual-Slope QRS detection algorithm with low computational complexity, suitable for wearable ECG devices. The Dual-Slope algorithm calculates the slopes on both sides of a peak in the ECG signal; And based on these slopes, three criterions are developed for simultaneously checking 1)Steepness 2)Shape and 3)Height of the signal, to locate the QRS complex. The algorithm, evaluated against MIT/BIH Arrhythmia Database, achieves a very high detection rate of 99.45%, a sensitivity of 99.82% and a positive prediction of 99.63%.

A 3-Lead ECG-on-Chip with QRS Detection and Lossless Compression for Wireless Sensors

This brief presents the design of a low-power 3-lead electrocardiogram (ECG)-on-chip with integrated real-time QRS detection and lossless data compression for wearable wireless ECG sensors. Data compression and QRS detection can reduce the sensor power by up to 2-5 times. A joint QRS detection and lossless data compression circuit allows computational resources to be shared among multiple functions, thus lowering the overall system power. The proposed technique achieves an average compression ratio of 2.15 times on standard test data. The QRS detector achieves a sensitivity (Se) of 99.58% and positive productivity (+P) of 99.57% @ 256 Hz when tested with the MIT/BIH database. Implemented in 0.35 μm process, the circuit consumes 0.96 μW @ 2.4 V with a core area of 1.56 mm2 for two-channel ECG compression and QRS detection. Small size and ultralow-power consumption makes the chip suitable for usage in wearable/ambulatory ECG sensors.

An ECG-SoC with 535nW/channel lossless data compression for wearable sensors

This paper presents a low power ECG recording Sys-tem-on-Chip (SoC) with on-chip low complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices. The proposed algorithm uses a linear slope predictor to estimate the ECG samples, and uses a novel low complexity dynamic coding-packaging scheme to frame the resulting estimation error into fixed-length 16-bit format. The proposed technique achieves an average compression ratio of 2.25× on MIT/BIH ECG database. Implemented in 0.35 μm process, the compressor uses 0.565 K gates/channel occupying 0.4 mm2 for 4-channel, and consumes 535 nW/channel at 2.4V for ECG sampled at 512 Hz. Small size and ultra-low power consumption makes the proposed technique suitable for wearable ECG sensor application.

A low complexity lossless compression scheme for wearable ECG sensors

This paper presents a low complexity lossless ECG compression algorithm for data reduction in wireless ambulatory ECG sensors. The proposed algorithm uses a novel linear prediction technique for redundancy removal and a joint coding-packaging scheme for compaction of the residual prediction error. Multiple linear predictors are engaged simultaneously to track the incoming data and the best prediction estimate is adaptively chosen based on the temporal signal characteristics to minimize error. An improved dynamic coding-packaging scheme frames the resulting estimation error into fixed-length 16-bit format. The proposed technique achieves an average compression ratio of 2.38× on MIT/BIH ECG database. Low complexity and good compression performance makes the proposed technique suitable for wearable ambulatory ECG monitoring applications.

Live demonstration: An ECG-on-Chip for wearable wireless sensors

We are demonstrating a wearable wireless ECG sensor prototype based on a highly integrated, ultra-low power ECG-on-Chip. The demonstration setup (as shown in Fig. 1) of the prototype sensor consists of 1) ST-Electromedicina ST-10 ECG signal generator 2) Prototype Sensor with the proposed ECG-on-Chip 3) Gateway application running in a notebook PC / Android smartphone. The signal output from the ECG generator is amplified, converted to digital domain and compressed by the ECG-on-Chip and streamed live via Bluetooth. The signal is received by a notebook PC with Bluetooth interface running the gateway application. The compressed signal is decompressed by the gateway application and displayed for observation/diagnostic purposes. This data can be uploaded to cloud server for long-term storage and more complex signal analysis. The signal transmitted by the sensor can also be received by a smartphone application in Android to visualize and analyze the signal further. A live subject will wear one of the sensors to demonstrate the ECG acquisition under normal daily activities like walking, running, jumping. ECG signals acquired during activities are shown in Fig 2.

Real-Time, Personalized Anomaly Detection in Streaming Data for Wearable Healthcare Devices

Ubiquitous deployment of low cost wearable healthcare devices and proactive monitoring of vital physiological data, are widely seen as a solution for the high costs and risks associated with personal healthcare. The healthcare data generated from these sensors cannot be manually analyzed for anomalies by clinicians due to its scale and therefore automated techniques has to be developed. Present approaches in literature depends on accurate detection of features from the acquired signal which is not always realistic due to noisy nature of the ambulatory physiological data obtained from the sensors. In addition, present anomaly detection approaches require manual training of the system for each patient, due to inherent variations in the morphology of physiological signal for each user. In this chapter, we will first introduce the system architecture for wearable health-care monitoring systems and present discussions on various components involved. Then we discuss on the complexities involved in realizing these methods and highlight key features. We then present our experiences in extracting the ECG segments in real-time and detecting any anomalies in the streams. Particularly, we apply real-time signal processing methods and heuristics to estimate the boundary limits of individual beats from the streaming ECG data. We discuss the importance of designing methods, which are blind to inherent variations among multiple patients and less dependent on the accuracy of the feature extraction. The proposed methods are tested on public database from physionet (QTDB) to validate the quality of results. We highlight and discuss all the significant results and conclude the chapter by proposing some open-ended research questions to be addressed in the near future.

An ECG-on-chip with joint QRS detection & data compression for wearable sensors

This paper presents a low power 3-lead ECG-on-Chip with real-time QRS detection and lossless data compression for wearable wireless ECG sensors. The proposed chip uses a novel approach that embeds the data compression in the heart beat (QRS) detection process leading to improved energy efficiency. The proposed technique achieves an average compression ratio (CR) of 2.15x and a peak detection sensitivity (Se) of 99.58% and positive productivity (+P) of 99.57%. The chip consumes only 960nW for QRS detection and data compression for 2-channel of ECG making it the lowest power chip.