Hypertension and arrhythmia are chronic diseases, which can be effectively prevented and controlled only if the physiological parameters of the patient are constantly monitored, along with the full support of the health education and professional medical care. In this paper, a role-based intelligent mobile care system with alert mechanism in chronic care environment is proposed and implemented. The roles in our system include patients, physicians, nurses, and healthcare providers. Each of the roles represents a person that uses a mobile device such as a mobile phone to communicate with the server setup in the care center such that he or she can go around without restrictions. For commercial mobile phones with Bluetooth communication capability attached to chronic patients, we have developed physiological signal recognition algorithms that were implemented and built-in in the mobile phone without affecting its original communication functions. It is thus possible to integrate several front-end mobile care devices with Bluetooth communication capability to extract patients' various physiological parameters [such as blood pressure, pulse, saturation of haemoglobin (SpO2), and electrocardiogram (ECG)], to monitor multiple physiological signals without space limit, and to upload important or abnormal physiological information to healthcare center for storage and analysis or transmit the information to physicians and healthcare providers for further processing. Thus, the physiological signal extraction devices only have to deal with signal extraction and wireless transmission. Since they do not have to do signal processing, their form factor can be further reduced to reach the goal of microminiaturization and power saving. An alert management mechanism has been included in back-end healthcare center to initiate various strategies for automatic emergency alerts after receiving emergency messages or after automatically recognizing emergency messages. Within the time intervals in system setting, according to the medical history of a specific patient, our prototype system can inform various healthcare providers in sequence to provide healthcare service with their reply to ensure the accuracy of alert information and the completeness of early warning notification to further improve the healthcare quality. In the end, with the testing results and performance evaluation of our implemented system prototype, we conclude that it is possible to set up a complete intelligent health care chain with mobile monitoring and healthcare service via the assistance of our system.

A Mobile Care System With Alert Mechanism

The study of electrocardiogram (ECG) waveform amplitudes, timings and patterns has been the subject of intense research, for it provides a deep insight into the diagnostic features of the heart's functionality. In some recent works, a Bayesian filtering paradigm has been proposed for denoising and compression of ECG signals. In this paper, it is shown that this framework may be effectively used for ECG beat segmentation and extraction of fiducial points. Analytic expressions for the determination of points and intervals are derived and evaluated on various real ECG signals. Simulation results show that the method can contribute to and enhance the clinical ECG beat segmentation performance.

/pdf/a-model-based-bayesian-framework-for-ecg-beat-segmentation-3sejrl3pu2.pdf

A model-based Bayesian framework for ECG beat segmentation

Real-time ECG telemonitoring system design with mobile phone platform

A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining the precise position of the waveforms of an ECG signal with baseline wander is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and the unknown drift. A model-based approach handles these complications, therefore a method based on this concept has been developed and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model. Simulation results show that the proposed method has an average sensitivity of 99.58%, average detection accuracy of 99.64%, and specificity of 100%.

/pdf/model-based-fiducial-points-extraction-for-baseline-wandered-14ym2v2lal.pdf

Model-Based Fiducial Points Extraction for Baseline Wandered Electrocardiograms

This paper analyzes heart rate (HR) information from physiological tracings collected with a remote millimeter wave (mmW) I-Q sensor for biometric monitoring applications. A parameter optimization method based on the nonlinear Levenberg-Marquardt algorithm is used. The mmW sensor works at 94 GHz and can detect the vital signs of a human subject from a few to tens of meters away. The reflected mmW signal is typically affected by respiration, body movement, background noise, and electronic system noise. Processing of the mmW radar signal is, thus, necessary to obtain the true HR. The down-converted received signal in this case consists of both the real part (I-branch) and the imaginary part (Q-branch), which can be considered as the cosine and sine of the received phase of the HR signal. Instead of fitting the converted phase angle signal, the method directly fits the real and imaginary parts of the HR signal, which circumvents the need for phase unwrapping. This is particularly useful when the SNR is low. Also, the method identifies both beat-to-beat HR and individual heartbeat magnitude, which is valuable for some medical diagnosis applications. The mean HR here is compared to that obtained using the discrete Fourier transform.

A real-time heart rate analysis for a remote millimeter wave I-Q sensor

Sleep-related breathing disorders can cause heart rate changes known as cyclical variation. The heart rate variation of patients with obstructive sleep apnea syndrome (OSAS) is more prominent in sleep. For this reason, to analyze heart rate variability (HRV) of patients with sleep apnea is a very important issue that can assist physicians to diagnose and give suitable treatment for patients. In this paper, a novel QRS detection algorithm is developed and applied to the analysis for HRV of patients with sleep apnea. The advantageous of the proposed algorithm is the combination of digital filtering and reverse R wave detection techniques to enhance the accuracy of R wave detection and easily implement into portable ECG monitoring system with light complexities of computation. The proposed algorithm is verified by simulation and experimental results.

https://www.worldscientific.com/doi/pdf/10.4015/S101623720500038X?src=recsys

A novel qrs detection algorithm applied to the analysis for heart rate variability of patients with sleep apnea

In this paper, the orthogonality of coefficient matrices of wavelet filters is utilized to derive the energy equation for the relation between time-domain signal and its corresponding wavelet coefficients. Using the energy equation, the relationship between the wavelet coefficient error and the reconstruction error is obtained. The errors considered in this paper include the truncation error and quantization error. This not only helps to control the reconstruction quality but also brings two advantages: (1) It is not necessary to perform inverse transform to obtain the distortion caused by compression using wavelet transform and can thus reduce computation efforts. (2) By using the energy equation, we can search for a threshold value to attain a better compression ratio within the range of a pre-specified percent root-mean-square difference (PRD) value. A compression algorithm with run length encoding is proposed based on the energy equation. In the end, the Matlab software and MIT-BIH database are adopted to perform simulations for verifying the feasibility of our proposed method. The algorithm is also implemented on a DSP chip to examine the practicality and suitability. The required computation time of an ECG segment is less than 0.0786 ,s which is fast enough to process real-time signals. As a result, the proposed algorithm is applicable for implementation on mobile ECG recording devices.

Design and implementation of ecg compression algorithm with controllable percent root-mean-square difference

This paper describes an approach for evaluating the risk of components used in the Holter. Holter is a portable device for recording patients' electrocardiogram in medicine. Holter might hurt users as a result of a bad design, and might record incorrectly when malfunction happens. To prevent risk of injury, we analyze the potential failures of the Holter based on the Failure Mode and Effects Analysis, which is a risk assessment technique. Then, we calculate the Risk Priority Number (RPN) of each failure. According to the RPN, we give two strategies for reducing the risk. One is protection, and the other is alarm. The protection system is used to prevent hazards of the Holter from incorrect operations by the user, and the alarm system is used to detect the malfunction of the Holter. The experimental results show that the safety of the Holter is improved with these two circuits.

Example for mobile ecg holter design using fmea model

This paper presents the design and implementation of a tele-emergent system to provide constant monitoring of cardiac patients. Our system is consisted of a user integration device attached to the patient, a wireless sensor network (WSN) and a medical gateway installed in the patient's home, and a server set up in the hospital. The user integration device includes Electrocardiogram (ECG) signal acquisition and processing circuit utilizing a "Modified So and Chan" QRS detection algorithm to discriminate the R signal wave and calculate RR intervals to subsequently detect abnormal Heart Rate Variability (HRV), and a Bluetooth transmission module to communicate with WSN. The WSN which is consisted of wireless sensor nodes with power-saving scheme is used to relay the ECG data to the medical gateway for ubiquitous monitoring at home. The medical gateway includes the heart disease symptom recognition system to further analyze the ECG data and recognize the symptoms of serious heart diseases, and a General Packet Radio Service (GPRS) module with Multimedia Messaging Service (MMS) to communicate with the server in the hospital. The server in the hospital can passively process the MMS messages from the medical gateway, and actively send commands to the medical gateway to acquire the needed ECG data from the patient. With our integrated system it is possible to provide real-time and long-term monitoring of physiological data for cardiac patients which is vital for the patients' medical care.

A tele-emergent system for long-term ecg monitoring over wireless sensor network

In this paper, the deployment analysis considering the latency in data forwarding stage for an in-home healthcare network with wireless multi-hop relaying for long-term electrocardiogram (ECG) monitoring is presented and performed. By means of multi-hop transmission, the ECG data of elders or chronic patients with heart diseases can be automatically acquired by the mobile-care device and wirelessly relayed to the residential gateway via the proposed network at any time or any place at home. Then the residential gateway uploads data to the back-end care server through Internet to carry out the patient's health condition monitoring and the management of pathological data. In order to effectively and timely forward the ECG data to the residential gateway in the house, the aim of this paper is to estimate and determine the maximum hop count of relay nodes needed to be deployed to achieve the ECG data relaying in time and the optimal communication coverage in the house. Finally, the simulation and performance evaluation results are given to verify the analysis.

I-Chi Chou

Papers

A novel qrs detection algorithm applied to the analysis for heart rate variability of patients with sleep apnea

Design and implementation of ecg compression algorithm with controllable percent root-mean-square difference

Example for mobile ecg holter design using fmea model

A tele-emergent system for long-term ecg monitoring over wireless sensor network

Deployment analysis for wireless multi-hop home healthcare networks