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In this paper the application of statistical approach for anomaly detection in a wearable ECG monitor has been considered. The method is based on the usage of the pre-defined expected behavior of the monitored biomedical signal and its on-line comparison with real-time measurements. Such comparison can be implemented based on the statistical model of ECG signal. To test the accuracy of the proposed method a ProSim 8 simulator was used to generate ECG signals. Our experiments showed that critical anomalies in ECG, such as Cardiac Failure, different types of Arrhythmia, and ST-segment deviations can be detected with high precision (96~100%), while false positive rate over typical NSR signal is low (<;4%). The method is scalable in terms of required performance, power consumption, and the precision of anomaly detection to be suitable for the implementation in a wearable platform.

Observation of a patient's respiration signal can provide a clinician with the required information necessary to analyse a subject's wellbeing. Due to an increase in population number and the aging population demographic there is an increasing stress being placed on current healthcare systems. There is therefore a requirement for more of the rudimentary patient testing to be performed outside of the hospital environment. However due to the ambulatory nature of these recordings there is also a desire for a reduction in the number of sensors required to perform the required recording in order to be unobtrusive to the wearer, and also to use textile based systems for comfort. The extraction of a proxy for the respiration signal from a recorded electrocardiogram (ECG) signal has therefore received considerable interest from previous researchers. To allow for accurate measurements, currently employed methods rely on the availability of a clean artifact free ECG signal from which to extract the desired respiration signal. However, ambulatory recordings, made outside of the hospital-centric environment, are often corrupted with contaminating artifacts, the most degrading of which are due to subject motion. This paper presents the use of the ensemble empirical mode decomposition (EEMD) algorithm to aid in the extraction of the desired respiration signal. Two separate techniques are examined; 1) Extraction of the respiration signal directly from the noisy ECG 2) Removal of the artifact components relating to the subject movement allowing for the use of currently available respiration signal detection techniques. Results presented illustrate that the two proposed techniques provide significant improvements in the accuracy of the breaths per minute (BPM) metric when compared to the available true respiration signal. The error reduced from ± 5.9 BPM prior to the use of the two techniques to ± 2.9 and ± 3.3 BPM post processing using the EEMD algorithm techniques.

Arrhythmia (i.e., irregular cardiac beat) classification in electrocardiogram (ECG) signals is an important issue for heart disease diagnosis due to the non-invasive nature of the ECG exam. In this paper, we analyze and criticize the results of some arrhythmia classification methods presented in the literature in terms of how the samples are chosen for training/testing the classifier and the impact this choice has on their performance (i.e., accuracy/sensitivity/specificity). From our implementation, we also report new accuracies for these methods, establishing a new state-of-the-art method, in terms of results.

In this paper we propose a novel channel identification scheme for solving permutation indeterminacy introduced by Independent Component Analysis (ICA) for artifacts removal from recorded three channel ECG signals within the remote health monitoring environment. The proposed scheme does not depend on the definition of any specific artifact which is the case with the existing approach and therefore leads to more robust and generic solution to this problem. The proposed scheme has been validated using nine practical case studies and its robustness has been proved by comparison with the existing approach. Simulation results show that the proposed scheme works successfully for all the nine cases whereas the existing approach fails to identify the correct channel for four cases.

This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal.

Numerous methods have been reported for deriving respiratory information such as respiratory rate from the electrocardiogram (ECG). In this paper the authors present a real-time algorithm for estimation and removal of baseline wander (BW) noise and obtaining the ECG-derived respiration (EDR) signal for estimation of a patient's respiratory rate. This algorithm utilizes a real-time "T-P knot" baseline wander removal technique which is based on the repetitive backward subtraction of the estimated baseline from the ECG signal. The estimated baseline is interpolated from the ECG signal at midpoints between each detected R-wave. As each segment of the estimated baseline signal is subtracted from the ECG, a "flattened" ECG signal is produced for which the amplitude of each R-wave is analyzed. The respiration signal is estimated from the amplitude modulation of R-waves caused by breathing. Testing of the algorithm was conducted in a pseudo real-time environment using MATLAB(TM), and test results are presented for simultaneously recorded ECG and respiration recordings from the PhysioNet/PhysioBank Fantasia database. Test data from patients were chosen with particularly large baseline wander components to ensure the reliability of the algorithm under adverse ECG recording conditions. The algorithm yielded EDR signals with a respiration rate of 4.4 breaths/min. for Fantasia patient record f2y10 and 10.1 breaths/min. for Fantasia patient record f2y06. These were in good agreement with the simultaneously recorded respiration data provided in the Fantasia database thus confirming the efficacy of the algorithm.

Abdominal signals (ADS) recorded from pregnant women represent an important tool for monitoring the fetal heart rate (FHR) variability and the well-being state of the fetus, mainly because it has the advantage of being noninvasive. Thus, no risk is given during recording either for the mother or for the fetus, but complex signal processing steps are necessary, mainly due to the presence of the maternal ECG in the ADS, in order to achieve a clean fetal electrocardiogram (fECG). The paper presents an improved application of the Event Synchronous Canceller (ESC) for maternal electrocardiogram (mECG) suppression. An adaptive mECG template which also includes the P and T waves is considered for the ESC algorithm. ESC is able to perfectly separate the mECG even though the fetal beats overlap with the maternal QRS complex (mQRS). The algorithm is applied both on real ADS recorded during pregnancy and on simulated ADS data; the latter now uses simulation of all signal and noise components, including the fetal and maternal ECG. The modified ESC shows good results in extracting a cleaned fECG signal that can help in reducing the inconsistency in interpretation of FHR. Thus the false positive diagnosis regarding the health status of fetus is strongly reduced.

In this paper, we introduce a new automatic method for electrocardiogram (ECG) artifact elimination from the electroencephalogram (EEG) or the electrooculogram (EOG). It is based on a modification of the independent component analysis (ICA) algorithm which gives promising results while only using a single-channel EEG (or EOG) and the ECG. To check the effectiveness of our approach, we compared its correction rate with those obtained by ensemble average subtraction (EAS) and adaptive filtering (AF). For this purpose, we applied these algorithms to 10 excerpts of polysomnographic sleep recordings containing ECG artifacts and other typical artifacts (e.g. movement, sweat, respiration, etc.). Two hundred successive interference peaks were examined in each excerpt to compute correction rates. We found that our modified ICA was the most robust to various waveforms of cardiac interference and to the presence of others artifacts, with a correction rate of 91.0%, against 83.5% for EAS and 83.1% for AF.

Diaphragmatic electromyogram (EMGdi) signals give important information about the respiratory muscle pump, can be used as an indicator of neural respiratory drive, and have been postulated as a method of designing neurally-activated intelligent ventilators. However diaphragmatic EMG signals measured with an esophageal catheter tend to be contaminated by electrical signals from the heart-electrocardiogram (ECG). This paper presents a novel method of rapidly separating and enhancing the Electromyogram signals from the combined EMG and ECG signals recorded from an esophageal catheter based sensor. Independent Component Analysis (ICA) is used to separate the EMG and ECG signals, then further processing is used to extract the frequency of the patient's breathing and the relative magnitudes of diaphragmatic muscle activity. These signals have two applications, firstly in artificial ventilator systems and as a diagnostic tool for health professionals.

Denoising electrocardiographic (ECG) signals is an essential procedure prior to their analysis. In this paper, we present a normalized adaptive neural filter (NANF) for cancellation of artifacts in ECG signals. The normalized filter coefficients are updated by the steepest-descent algorithm; the adaptation process is designed to minimize the difference between second-order estimated output values and the desired artifact-free ECG signals. Empirical results with benchmark data show that the adaptive artifact canceller that includes the NANF can effectively remove muscle-contraction artifacts and high-frequency noise in ambulatory ECG recordings, leading to a high signal-to-noise ratio. Moreover, the performance of the NANF in terms of the root-mean-squared error, normalized correlation coefficient, and filtered artifact entropy is significantly better than that of the popular least-mean-square (LMS) filter.