The electrocardiogram (ECG) signals P-wave, QRS wave and T-wave all reflect the activity of the heart, and the analysis of ECG signals can provide basic information for the diagnosis and prevention of heart disease. In the work of this paper, frequency-modulated continuous-wave (FMCW) radar and deep learning network are utilized to acquire ECG signals non-contactly, and we propose an improved differential and cross multiply (DACM) algorithm and a multi-neighbor differentiator for extracting cardiac motion acceleration information, as well as a partitioned reconstruction network incorporating an attention mechanism of encoder-decoder to achieve ECG signal reconstruction. The design principle is a combination of signal segmentation and deep learning (Sequence-to-sequence and attention) called SS-S2SA. firstly, a segmentation algorithm is applied to segment the acceleration signal and the ECG signal synchronously, and then the cardiac motion acceleration signal is mapped to the ECG signal using the SS-S2SA network. The method proposed in this paper is demonstrated to reconstruct ECG signals more accurately and finely by training more than 18,000 acceleration signal segments from 10 healthy subjects and evaluating the predictions from 5 subjects. The average correlation coefficient between the predicted signal and the real signal is about 0.92, and the mean absolute error (MAE) of the timing of the P-peak, R-peak, and T-peak are 13.9 ms, 8.1 ms, and 11.1 ms, respectively.

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring body-surface-displacement signals by using frequency-modulated continuous-wave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

This paper presents a driver status monitoring (DSM) system with body channel communication (BCC) technology to acquire the driver's physiological condition. Specifically, a conductive thread, the receiving electrode, is sewn to the surface of the seat so that the acquired signal can be continuously detected. As a signal transmission medium, body channel characteristics using the conductive thread electrode were investigated according to the driver's pose and the material of the driver's pants. Based on this, a BCC transceiver was implemented using an analog frequency modulation (FM) scheme to minimize the additional circuitry and system cost. We analyzed the heart rate variability (HRV) from the driver's electrocardiogram (ECG) and displayed the heart rate and Root Mean Square of Successive Differences (RMSSD) values together with the ECG waveform in real-time. A prototype of the DSM system with commercial-off-the-shelf (COTS) technology was implemented and tested. We verified that the proposed approach was robust to the driver's movements, showing the feasibility and validity of the DSM with BCC technology using a conductive thread electrode.

Individuals can be identified by features extracted from an electrocardiogram (ECG). However, irregular palpitations due to stress or exercise decrease the identification accuracy due to distortion of the ECG waveforms. In this letter, we propose a human identification scheme based on the frequency spectrums of an ECG, which can successfully extract features and thus identify individuals even while exercising. For the proposed scheme, we demonstrate an accuracy rate of 99.8% in a controlled experiment with exercising subjects. This level of accuracy is achieved by determining the significant features of individuals with a random forest classifier. In addition, the effectiveness of the proposed scheme is verified using a publicly available ECG database. We show that the proposed scheme also achieves a high accuracy with this public database.

Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.

Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.

Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.

In this study, we propose a two-step approach to evaluate electromagnetic interference (EMI) with a wearable vital signal sensor. The two-step approach combines a quasi-static electromagnetic (EM) field analysis and an electric circuit analysis, and is applied to the EMI evaluation at frequencies below 1 MHz for our developed wearable electrocardiogram (ECG) to demonstrate its usefulness. The quasi-static EM field analysis gives the common mode voltage coupled from the incident EM field at the ECG sensing electrodes, and the electric circuit analysis quantifies a differential mode voltage at the differential amplifier output of the ECG detection circuit. The differential mode voltage has been shown to come from a conversion from the common mode voltage due to an imbalance between the contact impedances of the two sensing electrodes. When the contact impedance is resistive, the induced differential mode voltage increases with frequency up to 100kHz, and keeps constant after 100kHz, i.e., exhibits a high pass filter characteristic. While when the contact impedance is capacitive, the differential mode voltage exhibits a band pass filter characteristic with the maximum at frequency of around 150kHz. The differential voltage may achieve nearly 1V at the differential amplifier output for an imbalance of 30% under 10V/m plane-wave incident electric field, and completely mask the ECG signal. It is essential to reduce the imbalance as much as possible so as to prevent a significant interference voltage in the amplified ECG signal.

A reliable detection of atrial fibrillation (AF) in Electrocardiogram (ECG) monitoring systems is significant for early treatment and health risk reduction. Various ECG mining and analysis studies have addressed a wide variety of clinical and technical issues. However, there is still room for improvement mostly in two areas. First, the morphological descriptors not only between different patients or patient clusters but also within the same patient are potentially changing. As a result, the model constructed using an old training data no longer needs to be adjusted in order to identify new concepts. Second, the number and types of ECG parameters necessary for detecting AF arrhythmia with high quality encounter a massive number of challenges in relation to computational effort and time consumption. We proposed a mixture technique that caters to these limitations. It includes an active learning method in conjunction with an ECG parameter customization technique to achieve a better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed a sensitivity of 95.2%, a specificity of 99.6%, and an overall accuracy of 99.2%.

An antidictionary is particularly useful for data compression, and on-line electrocardiogram (ECG) lossless compression algorithms using antidictionaries have been proposed. They work in real-time with constant memory and give better compression ratios than traditional lossless data compression algorithms, while they only deal with ECG data on a binary alphabet. This paper proposes on-line ECG lossless compression for a given data on a finite alphabet. The proposed algorithm gives not only better compression ratios than those algorithms but also uses less computational space than they do. Moreover, the proposed algorithm work in real-time. Its effectiveness is demonstrated by simulation results.

This paper proposes a joint source-channel coding technology to transmit periodic vital information such as an electrocardiogram. There is an urgent need for a ubiquitous medical treatment space in which personalized medical treatment is automatically provided based on measured vital information. To realize such treatment and reduce the constraints on the patient, wireless transmission of vital information from a sensor device to a data aggregator is essential. However, the vital information has to be correctly conveyed through wireless channels. In addition, sensor devices are constrained by their battery power. Thus, a coding technique that provides robustness to noise, channel efficiency and low power consumption at encoding is essential. This paper presents a coding method that uses correlation of periodic vital information in the time domain, and provides a decoding scheme that uses the correlation as side information in a maximum a posteriori probability algorithm. Our results show that the proposed method provides better performance in terms of mean squared error after decoding in comparison to differential pulse-code modulation, and the uncoded case.

The heart rate variability (HRV) is a measure based on the time position of the electrocardiogram (ECG) R-waves. There is a discussion whether or not we can obtain the HRV pattern from blood pressure (BP). In this paper, we propose a method for estimating HRV from a BP signal based on a HIF algorithm and carrying out experiments to compare BP as an alternative measurement of ECG to calculate HRV. Based on the hypotheses that ECG and BP have the same harmonic behavior, we model an alternative HRV signal using a nonlinear algorithm, called heart instantaneous frequency (HIF). It tracks the instantaneous frequency through a rough fundamental frequency using power spectral density (PSD). A novelty in this work is to use fundamental frequency instead of wave-peaks as a parameter to estimate and quantify beat-to-beat heart rate variability from BP waveforms. To verify how the estimate HRV signals derived from BP using HIF correlates to the standard gold measures, i.e. HRV derived from ECG, we use a traditional algorithm based on QRS detectors followed by thresholding to localize the R-wave time peak. The results show the following: 1) The spectral error caused by misestimation of time by R-peak detectors is demonstrated by an increase in high-frequency bands followed by the loss of time domain pattern. 2) The HIF was shown to be robust against noise and nuisances. 3) By using statistical methods and nonlinear analysis no difference between HIF derived from BP and HRV derived from ECG was observed.

Fetal electrocardiogram (FECG) extraction is of vital importance in biomedical signal processing. A promising approach is blind source extraction (BSE) emerging from the neural network fields, which is generally implemented in a semi-blind way. In this paper, we propose a robust extraction algorithm that can extract the clear FECG as the first extracted signal. The algorithm exploits the fact that the FECG signal's kurtosis value lies in a specific range, while the kurtosis values of other unwanted signals do not belong to this range. Moreover, the algorithm is very robust to outliers and its robustness is theoretically analyzed and is confirmed by simulation. In addition, the algorithm can work well in some adverse situations when the kurtosis values of some source signals are very close to each other. The above reasons mean that the algorithm is an appealing method which obtains an accurate and reliable FECG.

In this paper, we propose an ECG waveform compression technique based on the matching pursuit. The matching pursuit is an iterative non-orthogonal signal expansion technique. A signal is decomposed to atoms in a function dictionary. The constraint to the dictionary is only the over-completeness to signals. The function dictionary can be defined to be best match to the structure of the ECG waveform. In this paper, we introduce the multiscale analysis to the implementation of inner product computations between signals and atoms in the matching pursuit iteration. The computational cost can be reduced by utilization of the filter bank of the multiscale analysis. We show the waveform approximation capability of the matching pursuit with multiscale analysis. We show that a simple 4-tap integer filter bank is enough to the approximation and compression of ECG waveforms. In ECG waveform compression, we apply the error feed-back procedure to the matching pursuit iteration to reduce the norm of the approximation error. Finally, actual ECG waveform compression by the proposed method are demonstrated. The proposed method achieve the compression by the factor 10 to 30. The compression ratio given by the proposed method is higher than the orthogonal wavelet transform coding in the range of the reconstruction precision lower than 9% in PRD.

The recording of electrocardiogram (ECG) signals for the purpose of finding arrhythmias takes 24 hours. Generally speaking, changes in R-R intervals are used to detect arrhythmias. Our purpose is to develop an algorithm which efficiently detects R-R intervals. This system uses the R-wave position to calculate R-R intervals and then detects any arrhythmias. The algorithm searches for only the short time duration estimated from the most recent R-wave position in order to detect the next R-wave efficiently. We call this duration a WINDOW. A WINDOW is decided according to a proposed search algorithm so that the next R-wave can be expected in the WINDOW. In a case in which an S-wave is enhanced for some reason such as the manner in which the electrodes are installed in the system, the S-wave positions are taken to calculate the peak intervals instead of the R-wave. However, baseline wander and noise contained in the ECG signal have a deterrent effect on the accuracy with which the R-wave or the S-wave position is determined. In order to improve detection, the ECG signal is preprocessed using a Band-Pass Filter (BPF) which is composed of simple Cascaded Integrator Comb (CIC) filters. The American Heart Association (AHA) database was used in the simulation with the proposed algorithm. Accurate detection of the R-wave position was achieved in 99% of cases and efficient extraction of R-R intervals was possible.

An electrocardiogram (ECG) data compression algorithm using a polygonal approximation and the template beat variation method (TBV) has been evaluated by reconstruction error and automatic interpretation. The algorithm combining SAPA3 with TBV (SAPA3/TBV) has superior compression performance in PRD and compression ratio. The reconstruction errors, defined as the difference of the amplitude and the time duration between the original ECG and the reconstructed one, are large at waves with small amplitude and/or gradual slopes such as the P wave. Tracing rebuilt from the compressed ECG has been analysed using the automatic interpretative program, and the diagnostic answers with the realated measurements have been compared with the results obtained on the original ECG. The data compression algorithms (SAPA3 and SAPA3/TBV) have been tested on 100 cases in the data base produced by CSE. The reconstruction errors are related to the diagnostic errors. The TBV method suppresses these errors and more than 90% of diagnostic agreements at the error limit of 15µV can be obtained.

The performances of BPNN (neural network trained by back propagation) and PCANN (neural network which computes principal component analysis) for ECG data compression have been investigated from several points of view. We have compared them with an existing data compression method TOMEK. We used MIT/BIH arrhythmia database as ECG data. Both BPNN and PCANN showed better results than TOMEK. They showed 1.1 to 1.4 times higher compression than TOMEK to achieve the same accuracy of reproduction (13.0% of PRD and 99.0% of CC). While PCANN showed better learning ability than BPNN in simple learning task, BPNN was a little better than PCANN regarding compression rates. Observing the reproduced waveforms, BPNN and PCANN had almost the same performance, and they were superior to TOMEK. The following characteristics were obtained from the experiments. Since PCANN is sensitive to the learning rate, we had to precisely control the learning rate while the learning is in progress. We also found the tendency that PCANN needs larger amount of iteration in learning than BPNN for getting the same performance. PCANN showed better learning ability than BPNN, however, the total learning cost were almost the same between BPNN and PCANN due to the large amount of iteration. We analyzed the connection weight patterns. Since PCANN has a clear mathematical background, its behavior can be explained theoretically. BPNN sometimes generated the connection weights which were similar to the principal components. We supposed that BPNN may occasionally generate those patterns, and performs well while doing that. Finally we concluded as follows. Although the difference of the performances is smal, it was always observed and PCANN never exceeded BPNN. When the ease of analysis or the relation to mathematics is important, PCANN is suitable. It will be useful for the study of the recorded data such as statistics.