An intelligent telecardiology system for offline wireless transmission and remote analysis of ECG
20 Sep 2012-Journal of Medical Engineering & Technology (Taylor & Francis)-Vol. 36, Iss: 7, pp 358-365
TL;DR: This paper illustrates a wireless system developed for offline ECG transmission and its remote processing and test results with synthetic ECG from PTB diagnostic ECG database (ptb-db) for short range of communication are reported.
Abstract: This paper illustrates a wireless system developed for offline ECG transmission and its remote processing. At the transmit end, a standalone embedded system collects short duration ECG samples from the source, stores them in a RAM and then encodes these using a customized bi-phase modulation for offline transmission in ISM band. At the receive end, another embedded system extracts ECG data from the demodulated output of the wireless receiver and delivers to a desktop computer using the serial port. An application software stores these samples in a text file. At first, the bit error and packet error are computed from the received dataset. Then, computed ECG wave durations and intervals along with lead data plot are generated on a Graphical User Interface for preliminary level diagnosis. Test results with synthetic ECG from PTB diagnostic ECG database (ptb-db) for short range of communication are reported.
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TL;DR: A short range wireless telecardiology system is described with the objective to transmit electrocardiogram signal for remote end acquisition using a combination of modified delta encoding and run length encoding technique.
Abstract: Remote monitoring of biomedical signals provides an opportunity to extend health care service to a distant patient. In this paper, a short range wireless telecardiology system is described with the objective to transmit electrocardiogram signal for remote end acquisition. The acquired signal was compressed using a combination of modified delta encoding and run length encoding technique and transmitted using a wireless transceiver operating in 2.4 GHz industrial, scientific and medical band to a distance of 400 ft. In the receiving end, error check principle was used to find any data loss before the data is reconstructed for feature extraction. With Physionet data using 8-bit quantization an average compression ratio (CR) of 12.23, percentage root mean squared difference (PRD) of 4.342 and PRD normalized (PRDN) of 9.271 were obtained. With ECG data collected from healthy volunteers, these figures came out to be 14.64, 12.92 and 13.46 respectively. An improvement of performance was observed with 10 bit quantization of ECG data. Computational simplicity of the proposed algorithm provides an opportunity to use a low end microcontroller to implement the compression in standalone hardware.
16 citations
TL;DR: In this article, the authors presented a cloud-centric solution to provide remote monitoring of cardiovascular diseases (CVDs) through a mobile gateway, where the ECG data are transmitted to S3 bucket provided by Amazon web service (AWS).
Abstract: Statistical reports all around the world have deemed cardiovascular diseases (CVDs) as the largest contributor to the death count. The electrocardiogram (ECG) is a widely accepted technology employed for investigation of CVDs of the person. The proposed solution deals with an efficient internet of things (IoT) enabled real-time ECG monitoring system using cloud computing technologies. The article presents a cloud-centric solution to provide remote monitoring of CVD. Sensed ECG data are transmitted to S3 bucket provided by Amazon web service (AWS) through a mobile gateway. AWS cloud uses HTTP and MQTT servers to provide data visualisation, quick response and long-live connection to device and user. Bluetooth low energy (BLE 4.0) is used as a communication protocol for low-power data transmission between device and mobile gateway. The proposed system is implemented with filtering algorithms to ignore distractions, environmental noise and motion artefacts. It offers an analysis of ECG signals to detect various parameters such as heartbeat, PQRST wave and QRS complex intervals along with respiration rate. The proposed system prototype has been tested and validated for reliable ECG monitoring remotely in real-time.
15 citations
TL;DR: The development of a biomedical sensor node (BSN) for short-range monitoring of static cardiovascular patients using a supervisory computer is described and the developed system can provide a low-cost solution for patient monitoring at indoor hospital wards in developing nations like India.
Abstract: Wireless human health monitoring using different techniques has been a popular research area in the last few decades. In this paper, we describe the development of a biomedical sensor node (BSN) fo...
12 citations
01 Dec 2018
TL;DR: A multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD).
Abstract: In this work, a multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD). The beat detection procedure was inspired by the Pan-Tompkins algorithm with some necessary modifications. A lead-wise PCA decomposition was performed for dimensionality reduction with a single beat from each lead at a time using a fixed energy reconstruction criteria. The optimal quantization levels of the principal components were allocated using multi-layer perceptron neural network (MLP-NN) using lead clinical features as the input. This MLP-NN was tuned offline by a particle swarm optimization (PSO) generated data for quantization level of coefficients of PC as the reference. The proposed technique was evaluated using 8 types of cardiac abnormalities record from multi-lead ECG data from the PTB Diagnostic ECG database, with an average CR, PRD and PRDN of 16.2, 1.47% and 1.84% respectively. The reconstructed records were clinically acceptable. The proposed technique provides superior performance than few recent published works on multilead ECG compression.
10 citations
Cites background from "An intelligent telecardiology syste..."
...A second application of compression is in telemonitoring systems, where the communication link efficiency can be enhanced and local buffer requirement can be reduced [2], [3], [4]....
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TL;DR: A quality controlled compression of multilead electrocardiogram (MECG) is proposed, based on tensor analysis, and implemented upon 3D beat tensor of MECG, and has provided superior result as compared to recently published works on M ECG data compression.
9 citations
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"An intelligent telecardiology syste..." refers background in this paper
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78 citations