Short range centralized cardiac health monitoring system based on ZigBee communication
01 Dec 2014-pp 177-182
TL;DR: A short range centralized health monitoring system to acquire electrocardiogram (ECG) data using wireless ZigBee communication for computerized analysis of patient modules and post acquisition data analysis is described.
Abstract: Remote health monitoring is a prominent area in modern biomedical research. This involves collection in different biomedical signals from patient using information and communication technology with the objective of remote end assessment of these vital conditions. This paper describes a short range centralized health monitoring system to acquire electrocardiogram (ECG) data using wireless ZigBee communication for computerized analysis. A prototype compact patient data collection system based on ATmega16L microcontroller was developed to collect and compress single lead ECG data for wireless transfer to a centralized station for remote end processing. A state of the art developed software in the central station controlled the patient modules and post acquisition data analysis. Test results with Physionet data and ECG collected from volunteers shown satisfactory result. Average compression achieved using 70 ECG files was 6.93 with average PRD and PRDN of 1.1343 and 8.4645 respectively. Feature extraction results using receiving end ECG data showed an average variance of 0.12%.
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01 Jan 2016
TL;DR: Monitoring patient's body temperature, respiration rate, heart beat and body movement using Raspberry Pi board brings out the solution for effective patient monitoring at reduced cost and also reduces the trade-off between patient outcome and disease management.
Abstract: In the recent development of, Internet of Things (IoT) makes all objects interconnected and it has been recognized as the next technical revolution Some of the applications of Internet of Things are smart parking, smart home, smart city, smart environment, industrial places, agriculture fields and health monitoring process One such application is in healthcare to monitor the patient health status Internet of Things makes medical equipments more efficient by allowing real time monitoring of patient health, in which sensor acquire data of patient's and reduces the human error In Internet of Things patient's parameters get transmitted through medical devices via a gateway, where it is stored and analyzed The significant challenges in the implementation of Internet of Things for healthcare applications is monitoring all patient's from various places Thus Internet o Things in the medical field brings out the solution for effective patient monitoring at reduced cost and also reduces the trade-off between patient outcome and disease management In this paper discuss about, monitoring patient's body temperature, respiration rate, heart beat and body movement using Raspberry Pi board
135 citations
Cites background from "Short range centralized cardiac hea..."
...Keywords—Raspberry Pi board, Heartbeat sensor, Temperature sensor, Respiration sensor, Accelerometer sensor, Internet of Things....
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01 Sep 2017
TL;DR: An intelligent patient monitoring system for monitoring the patients' health condition automatically through sensors based connected networks that can able to detect the critical condition of a patient by processing sensors data and instantly provides push notification to doctors/nurses as well as hospital in-charge personal.
Abstract: The popularity of Internet of Things is increasing day by day in the area of remote monitoring system. The remote monitoring systems include, vehicle or assets monitoring, kids/pets monitoring, fleet management, parking management, water and oil leakage, energy grid monitoring etc. In this paper, we have proposed an intelligent patient monitoring system for monitoring the patients' health condition automatically through sensors based connected networks. Several sensors are used for gathering the biological behaviors of a patient. The meaningful biological information are then forwarded to the IoT cloud. The system is more intelligent that can able to detect the critical condition of a patient by processing sensors data and instantly provides push notification to doctors/nurses as well as hospital in-charge personal. The doctors and nurses get benefited from this system by observing their corresponding patients remotely without visiting in person. Patients' relatives can also get benefited from this system with limited access.
52 citations
Cites background from "Short range centralized cardiac hea..."
...Nowadays, various health monitoring devices are getting wearable/portable, including body temperature monitors, glucose monitors, ECG monitors, pulse oximeters, and blood pressure monitoring system are described in [11–19]....
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01 Feb 2019
TL;DR: Remote patient health monitoring system is an IoT device which could be used with patients or elderly at the authors' homes whose real time health readings such as temperature, blood pressure and electro-cardiogram could be monitored remotely on a hand held device.
Abstract: Remote patient health monitoring system is an IoT device which could be used with patients or elderly at our homes whose real time health readings such as temperature, blood pressure and electro-cardiogram could be monitored remotely on a hand held device. This IoT device will automatically send alert to the users in case of an emergency which in this case would be fluctuation of the readings of the sensors beyond the normal range. This device is build using thermometer, electro-cardiogram sensor and sphygmomanometer attached to an arduino which transfer its data to servers using a wifi-module. The servers then compute the data which can be displayed on hand held devices. In case the values received from the sensors is outside the normal range then an alert will be sent to the user from the server.
16 citations
Cites methods from "Short range centralized cardiac hea..."
...al, [7] made the use of AT Mega 16L microcontroller for the monitoring of electrocardiogram waves of a patient....
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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 "Short range centralized cardiac hea..."
...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 aware compression method of single lead ECG is described using principal component analysis (PCA), which yields better results than recently published works on quality controlled ECG compression.
Abstract: Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.
10 citations
Cites methods from "Short range centralized cardiac hea..."
...and easy to implement using low end processors [10]....
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References
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TL;DR: The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
Abstract: Electrocardiogram (ECG) compression techniques are compared, and a unified view of these techniques is established. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are ECG differential pulse code modulation (DPCM) and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods include Fourier, Walsh, and Karhunen-Loeve transforms. The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods. A framework for evaluation and comparison of ECG compression schemes is presented. >
690 citations
"Short range centralized cardiac hea..." refers background in this paper
...ECG compression schemes [11] are of generally three types: direct data compression, transformation type, and parameter extraction....
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TL;DR: Despite the many problems still encountered such as technological problems related to sensors and some problems like battery replacement, the ZigBee could be one of components of wireless u-healthcare systems in the future due to its advantages of lower power consumption.
Abstract: Objective To investigate the efficacy of a u-healthcare service using Zigbee and mobile phone for elderly patients with diabetes mellitus or heart diseases. Materials and methods From July to October, 2005, 29 patients were enrolled in our study. Two selected u-healthcare items, ECG and blood glucose measurement, were monitored. Twenty patients were provided with ZigBee built-in blood glucometer and mobile phones, and were instructed on using a web service where the measured blood glucose could be transmitted directly to the web and be administrated. Nine patients participated in ECG monitoring, by using a wireless, transmittable ECG recording instrument equipped with ZigBee protocol attached to their chest. Daily average transmission frequency, rate of transmission loss, and error reasons were analyzed. In addition, the patients were asked to score their degree of satisfaction about the sensors and u-healthcare services. Results The mean transmission frequencies were 2.1 times/day in blood glucose monitoring and 6.1 times/day in ECG. The patients’ satisfaction scores of the blood glucometer and service used in this research were 8.59 and 9.01 of 10 points, respectively. The mean satisfaction scores about ECG sensor and ECG monitoring services were 5.79 and 7.29, respectively. Discussion Despite the many problems still encountered such as technological problems related to sensors and some problems like battery replacement, we could transfer the data of glucometer and ECG sensors to web-server via ZigBee protocol. Authors think the ZigBee could be one of components of wireless u-healthcare systems in the future due to its advantages of lower power consumption.
195 citations
"Short range centralized cardiac hea..." refers methods in this paper
...Wearable sensor and mobile platform are also used for monitoring of elderly patients [3]....
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01 Jan 2012
TL;DR: A reliable transmission protocol based on anycast routing for wireless patient monitoring, which integrates fall detection, indoor positioning, and ECG monitoring and is fast and reliable and can seamlessly integrate with the next generation technology of wireless wide area network, worldwide interoperability for microwave access.
Abstract: Patient monitoring systems are gaining their importance as the fast-growing global elderly population increases demands for caretaking. These systems use wireless technologies to transmit vital signs for medical evaluation. In a multihop ZigBee network, the existing systems usually use broadcast or multicast schemes to increase the reliability of signals transmission; however, both the schemes lead to significantly higher network traffic and end-to-end transmission delay. In this paper, we present a reliable transmission protocol based on anycast routing for wireless patient monitoring. Our scheme automatically selects the closest data receiver in an anycast group as a destination to reduce the transmission latency as well as the control overhead. The new protocol also shortens the latency of path recovery by initiating route recovery from the intermediate routers of the original path. On the basis of a reliable transmission scheme, we implement a ZigBee device for fall monitoring, which integrates fall detection, indoor positioning, and ECG monitoring. When the triaxial accelerometer of the device detects a fall, the current position of the patient is transmitted to an emergency center through a ZigBee network. In order to clarify the situation of the fallen patient, 4-s ECG signals are also transmitted. Our transmission scheme ensures the successful transmission of these critical messages. The experimental results show that our scheme is fast and reliable. We also demonstrate that our devices can seamlessly integrate with the next generation technology of wireless wide area network, worldwide interoperability for microwave access, to achieve real-time patient monitoring.
125 citations
"Short range centralized cardiac hea..." refers methods in this paper
...In [4], an effort to minimize the transmission latency in a network using anycast network system was describe....
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TL;DR: It is demonstrated that for the low sample rate and coarse quantization required for ambulatory recording, without sufficient temporal resolution in beat location, beat subtraction does not significantly improve compression, and may even worsen compression performance.
Abstract: A strategy is evaluated for compression of ambulatory electrocardiograms (ECGs) that uses average beat subtraction and Huffman coding of the differenced residual signal. A sample rate of 100 sps and a quantization level of 35 mu V are selected to minimize the mean-square-error distortion while maintaining a data rate that allows 24 h of two-channel ECG data to be stored in less than 4 MB of memory. With this method, sample rate, and quantization level, the ambulatory ECG is compressed and stored in real time with an average data rate of 174 b/s per channel. It is demonstrated that, for the low sample rate and coarse quantization required for ambulatory recording, without sufficient temporal resolution in beat location, beat subtraction does not significantly improve compression, and may even worsen compression performance. It is also shown that with average beat subtraction, compression is improved if multiple beat averages maintained. Improvement is most significant for ECG signals that exhibit frequent ectopic beats. >
117 citations
"Short range centralized cardiac hea..." refers methods in this paper
...In [12], authors are described a compression techniques using average beat subtraction and first differencing of residual data....
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01 Dec 2011
TL;DR: An offline ECG compression technique, based on encoding of successive sample differences is proposed, which is presently being implemented in a wireless telecardiology system using a standalone embedded system.
Abstract: An offline ECG compression technique, based on encoding of successive sample differences is proposed. The encoded elements are generated through four stages, viz., down sampling of raw samples, normalization of successive sample differences; data grouping; magnitude and sign encoding; and finally zero element compression. Initially, the compression algorithm is validated with short duration raw ECG samples from PTB database under Physionet. MATLAB simulation results using ptb-db data with 8-bit quantization results a compression ratio (CR) of 9.02 and percentage root mean square difference (PRD) of 2.51. With mit-db these figures are 4.68 and 0.739 respectively. The algorithm is presently being implemented in a wireless telecardiology system using a standalone embedded system.
67 citations