Beat-to-beat heart rate detection in multi-lead abdominal fetal ECG recordings

Reliable monitoring of fetal condition often requires more information than is provided by cardiotocography, the standard technique for fetal monitoring. Abdominal recording of the fetal electrocardiogram may offer valuable additional information, but unfortunately is troubled by poor signal-to-noise ratios during certain parts of pregnancy. To increase the usability of abdominal fetal ECG recordings, an algorithm was developed that enhances fetal QRS complexes in these recordings and thereby provides a promising method for detecting the beat-to-beat fetal heart rate in recordings with poor signal-to-noise ratios. The method was evaluated on generated recordings with controlled signal-to-noise ratios and on actual recordings that were performed in clinical practice and were annotated by two independent experts. The evaluation on the generated signals demonstrated excellent results (sensitivity of 0.98 for SNR≥1.5). Only for SNR<2, the inaccuracy of the fetal heart rate detection exceeded 2 ms, which may still suffice for cardiotocography but is unacceptable for analysis of the beat-to-beat fetal heart rate variability. The sensitivity and positive predictive value of the method in actual recordings were reduced to approximately 90% for SNR≤2.4, but were excellent for higher signal-to-noise ratios.     

A Construction Method of Personalized ECG Template and Its Application in Premature Ventricular Contraction Recognition for ECG Mobile Phones

Premature ventricular contraction(PVC) is the most frequent arrhythmia encountered in clinical practice. PVC may occur in health subjects, which is not imminently life-threatening but may require therapies to prevent further problems. So,the timely PVC recognition becomes very important for the analysis of electrocardiogram(ECG), especially for the remote ECG monitoring using mobile phones. In this paper,a construction method of personalized ECG template and a PVC recognition method based on template matching were studied. Firstly, we selected 43 ECG recordings from the MIT-BIH arrhythmia database. All recordings were divided into two datasets(DS1for training and DS2 for testing) and each dataset approximately contained the same proportion of PVC beats. Subsequently, for each recording(30 min) in DS1, the first5 min recordings were used to construct the personalized ECG template and the last25 min recordings were used for the R-wave peaks detection and PVC recognition,where the template matching method were used. The validity of the proposed methods was tested using DS2. The results showed that: 1) high beat detection accuracy was achieved for both PVC beats and non-PVC beats; 2) the sensitivity and specificity of PVC recognition were 99.11% and 99.96% for the first 5 min recordings respectively,99.17% and 99.43% for the last 25 min recordings respectively. All the proposed methods can be real-time performed, which show a promising prospect for the application of ECG mobile phones.     

Deep Arm/Ear-ECG Image Learning for Highly Wearable Biometric Human Identification

In this study, to advance smart health applications which have increasing security/privacy requirements, we propose a novel highly wearable ECG-based user identification system, empowered by both non-standard convenient ECG lead configurations and deep learning techniques. Specifically, to achieve a super wearability, we suggest situating all the ECG electrodes on the left upper-arm, or behind the ears, and successfully obtain weak but distinguishable ECG waveforms. Afterwards, to identify individuals from weak ECG, we further present a two-stage framework, including ECG imaging and deep feature learning/identification. In the former stage, the ECG heartbeats are projected to a 2D state space, to reveal heartbeats’ trajectory behaviors and produce 2D images by a split-then-hit method. In the second stage, a convolutional neural network is introduced to automatically learn the intricate patterns directly from the ECG image representations without heavy feature engineering, and then perform user identification. Experimental results on two acquired datasets using our wearable prototype, show a promising identification rate of 98.4% (single-arm-ECG) and 91.1% (ear-ECG), respectively. To the best of our knowledge, it is the first study on the feasibility of using single-arm-ECG/ear-ECG for user identification purpose, which is expected to contribute to pervasive ECG-based user identification in smart health applications.     

A Methodology for Detecting Field Potentials from the External Ear Canal: NEER and EVestG

An algorithm called the neural event extraction routine (NEER) and a method called Electrovestibulography (EVestG) for extracting field potentials (FPs) from artefact rich and noisy ear canal recordings is presented. Averaged FP waveforms can be used to aid detection of acoustic and or vestibular pathologies. FPs were recorded in the external ear canal proximal to the ear drum. These FPs were extracted using an algorithm called NEER. NEER utilises a modified complex Morlet wavelet analysis of phase change across multiple scales and a template matching (matched filter) methodology to detect FPs buried in noise and biological and environmental artefacts. Initial simulation with simulated FPs shows NEER detects FPs down to -30 dB SNR (power) but only 13-23% of those at SNR's <-6 dB. This was deemed applicable to longer duration recordings wherein averaging could be applied as many FPs are present. NEER was applied to detect both spontaneous and whole body tilt evoked FPs. By subtracting the averaged tilt FP response from the averaged spontaneous FP response it is believed this difference is more representative of the vestibular response. Significant difference (p < 0.05) between up and down whole body (supine and sitting) movements was achieved. Pathologic and physiologic evidence in support of a vestibular and acoustic origin is also presented.     

An automated ECG-artifact removal method for trunk muscle surface EMG recordings

This study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30 Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p < 0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits.     

Design of a wavelet interpolation filter for enhancement of the ST-segment

A wavelet interpolation filter (WIF) is designed for the removal of motion artifacts in the ST-segment of stress ECGs. The WIF consists of two parts. One part is a wavelet transform that decomposes the stress ECG signal into several frequency bands using a Haar wavelet. The other part is an interpolation method, such as the spline technique, that is used to enhance the reconstruction performance of the signal decomposed by the wavelet transform. To evaluate the performance of the WIF, three indices are used: signal-to-noise ratio (SNR), reconstruction square error (RSE) and standard deviation (SD). The MIT/BIH arrhythmia database, the European ST-T database and the triangular wave are used for evaluation. A noisy ECG signal, corrupted by motion artifacts, is simulated by the addition of two types of random noise to the original ECG signal. For comparison, three indices for the other methods are also computed: mean, median and hard thresholding. The performance of the WIF shows that RSE, SNR and SD are 392.7, 18.3 dB and 2.6, respectively, in the case of a noisy signal with an SNR of 7.1 dB. This result is much better than those for the other methods.     

Comparison of finger plethysmograph to ECG in the measurement of heart rate variability

Two experiments compared finger plethysmograph (FP) to electrocardiogram (ECG) in providing accurate heart periods for use in heart rate variability (HRV) calculations. In Experiment 1, simultaneous ECG and FP recordings were taken from 16 healthy subjects at rest. In Experiment 2, 10 additional healthy subjects were recorded at rest and during the Stroop Color-Word Test. In both studies, high correlations were found between FP-derived and ECG-derived band variance for high and low frequency HRV at rest. But, during the Stroop task, correlations were strongly diminished. In addition, under both conditions, HRV measures were significantly higher using the FP signal. Thus, FP may be adequate for determining HRV at rest, but, for experimental use, ECG may still be recommended. Nonetheless, further studies that include test-retest reliability assessment of both data collection techniques are warranted before a more certain determination can be made.     

RPCA-based detection and quantification of motion artifacts in ECG signals

In this paper, a recursive principal component analysis (RPCA)-based algorithm is applied for detecting and quantifying the motion artifact episodes encountered in an ECG signal. The motion artifact signal is synthesized by low-pass filtering a random noise signal with different spectral ranges of LPF (low pass filter): 0–5 Hz, 0–10 Hz, 0–15 Hz and 0–20 Hz. Further, the analysis of the algorithm is carried out for different values of SNR levels and forgetting factors (α) of an RPCA algorithm. The algorithm derives an error signal, wherever a motion artifact episode (noise) is present in the entire ECG signal with 100% accuracy. The RPCA error magnitude is almost zero for the clean signal portion and considerably high wherever the motion artifacts (noisy episodes) are encountered in the ECG signals. Further, the general trend of the algorithm is to produce a smaller magnitude of error for higher SNR (i.e. low level of noise) and vice versa. The quantification of the RPCA algorithm has been made by applying it over 25 ECG data-sets of different morphologies and genres with three different values of SNRs for each forgetting factor and for each of four spectral ranges.     

Arduino-based noise robust online heart-rate detection

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.     

Enhancement of low-quality fetal electrocardiogram based on time-sequenced adaptive filtering

Extraction of a clean fetal electrocardiogram (ECG) from non-invasive abdominal recordings is one of the biggest challenges in fetal monitoring. An ECG allows for the interpretation of the electrical heart activity beyond the heart rate and heart rate variability. However, the low signal quality of the fetal ECG hinders the morphological analysis of its waveform in clinical practice. The time-sequenced adaptive filter has been proposed for performing optimal time-varying filtering of non-stationary signals having a recurring statistical character. In our study, the time-sequenced adaptive filter is applied to enhance the quality of multichannel fetal ECG after the maternal ECG is removed. To improve the performance of the filter in cases of low signal-to-noise ratio (SNR), we enhance the ECG reference signals by averaging consecutive ECG complexes. The performance of the proposed augmented time-sequenced adaptive filter is evaluated in both synthetic and real data from PhysioNet. This evaluation shows that the suggested algorithm clearly outperforms other ECG enhancement methods, in terms of uncovering the ECG waveform, even in cases with very low SNR. With the presented method, quality of the fetal ECG morphology can be enhanced to the extent that the ECG might be fit for use in clinical diagnostics.

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Graphical abstract The extracted fetal ECG signals from non-invasive abdominal recordings still contain a substantial amount of noise. The time-sequenced adaptive filter provides a relatively accurate estimate of the underlying fetal ECG signal when the quality of the reference channels is enhanced prior to filtering.

     

ECG signal denoising and baseline wander correction based on the empirical mode decomposition

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT-BIH databases. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal.     

Prognostic value of heart rate variability after myocardial infarction. A comparison of different data-processing methods

Reduced heart rate variability (HRV) has been reported as a predictor of mortality in recent myocardial infarction patients. However, its automated assessment in long-term ECG recordings is complicated by recording noise and beat-recognition errors which necessitate filtering of the computer-established sequence of beat-to-beat intervals, and visual checking and manual editing of the long-term recordings, making the whole method operator-dependent. To develop a fully automated method for analysis of HRV from 24 h ECG recordings, five filtering algorithms were combined with three methods of expressing HRV numerically and used to compare two groups of patients undergoing 24 h tape recordings of the ECG within the first two weeks after myocardial infarction. One group comprised 15 patients who later suffered death or ventricular tachycardia, the other group comprised 15 randomly selected uncomplicated cases. Using the same two groups of patients, three different methods of expressing HRV on a beat-to-beat basis were also compared empirically. The results show that alternative, operator-independent methods for establishing HRV from continuous long-term ECG recordings of postmyocardial infarction patients seem to be as effective as previously reported methods which rely on operator-dependent data post-processing techniques.     

Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system

We present a novel unbiased and normalized adaptive noise reduction (UNANR) system to suppress random noise in electrocardiographic (ECG) signals. The system contains procedures for the removal of baseline wander with a two-stage moving-average filter, comb filtering of power-line interference with an infinite impulse response (IIR) comb filter, an additive white noise generator to test the system's performance in terms of signal-to-noise ratio (SNR), and the UNANR model that is used to estimate the noise which is subtracted from the contaminated ECG signals. The UNANR model does not contain a bias unit, and the coefficients are adaptively updated by using the steepest-descent algorithm. The corresponding adaptation process is designed to minimize the instantaneous error between the estimated signal power and the desired noise-free signal power. The benchmark MIT-BIH arrhythmia database was used to evaluate the performance of the UNANR system with different levels of input noise. The results of adaptive filtering and a study on convergence of the UNANR learning rate demonstrate that the adaptive noise-reduction system that includes the UNANR model can effectively eliminate random noise in ambulatory ECG recordings, leading to a higher SNR improvement than that with the same system using the popular least-mean-square (LMS) filter. The SNR improvement provided by the proposed UNANR system was higher than that provided by the system with the LMS filter, with the input SNR in the range of 5-20 dB over the 48 ambulatory ECG recordings tested.     

Denoising of HD-sEMG signals using canonical correlation analysis

High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.     

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Sleep apnoea is a common disorder that is usually diagnosed through expensive studies conducted in sleep laboratories. Sleep apnoea is accompanied by a characteristic cyclic variation in heart rate or other changes in the waveform of the electrocardiogram (ECG). If sleep apnoea could be diagnosed using only the ECG, it could be possible to diagnose sleep apnoea automatically and inexpensively from ECG recordings acquired in the patient's home. This study had two parts. The first was to assess the ability of an overnight ECG recording to distinguish between patients with and without apnoea. The second was to assess whether the ECG could detect apnoea during each minute of the recording. An expert, who used additional physiological signals, assessed each of the recordings for apnoea. Research groups were invited to access data via the world-wide web and submit algorithm results to an international challenge linked to a conference. A training set of 35 recordings was made available for algorithm development, and results from a test set of 35 different recordings were made available for independent scoring. Thirteen algorithms were compared. The best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform. Four of these algorithms achieved perfect scores of 100% in the first part of the study, and two achieved an accuracy of over 90% in the second part of the study.     

Sensitivity to simulated directional sound motion in the rat primary auditory cortex.

Sensitivity to simulated directional sound motion in the rat primary auditory cortex. This paper examines neuron responses in rat primary auditory cortex (AI) during sound stimulation of the two ears designed to simulate sound motion in the horizontal plane. The simulated sound motion was synthesized from mathematical equations that generated dynamic changes in interaural phase, intensity, and Doppler shifts at the two ears. The simulated sounds were based on moving sources in the right frontal horizontal quadrant. Stimuli consisted of three circumferential segments between 0 and 30 degrees, 30 and 60 degrees, and 60 and 90 degrees and four radial segments at 0, 30, 60, and 90 degrees. The constant velocity portion of each segment was 0.84 m long. The circumferential segments and center of the radial segments were calculated to simulate a distance of 2 m from the head. Each segment had two trajectories that simulated motion in both directions, and each trajectory was presented at two velocities. Young adult rats were anesthetized, the left primary auditory cortex was exposed, and microelectrode recordings were obtained from sound responsive cells in AI. All testing took place at a tonal frequency that most closely approximated the best frequency of the unit at a level 20 dB above the tuning curve threshold. The results were presented on polar plots that emphasized the two directions of simulated motion for each segment rather than the location of sound in space. The trajectory exhibiting a "maximum motion response" could be identified from these plots. "Neuron discharge profiles" within these trajectories were used to demonstrate neuron activity for the two motion directions. Cells were identified that clearly responded to simulated uni- or multidirectional sound motion (39%), that were sensitive to sound location only (19%), or that were sound driven but insensitive to our location or sound motion stimuli (42%). The results demonstrated the capacity of neurons in rat auditory cortex to selectively process dynamic stimulus conditions representing simulated motion on the horizontal plane. Our data further show that some cells were responsive to location along the horizontal plane but not sensitive to motion. Cells sensitive to motion, however, also responded best to the moving sound at a particular location within the trajectory. It would seem that the mechanisms underlying sensitivity to sound location as well as direction of motion converge on the same cell.     

Identification of myocardial infarction (MI) using spatio-temporal heart dynamics

Cardiovascular disorders, such as myocardial infarction (MI) are the leading causes of mortality in the world. This paper presents an approach that uses novel spatio-temporal patterns of the vectorcardiogram (VCG) signals for the identification of various types of MI. In contrast to the traditional electrocardiogram (ECG) approaches, the 3D cardiac VCG signal is partitioned into 8 octants for localized analysis of the heart's electrical activities. The proposed method was tested using the PhysioNet PTB database for 368 MIs and 80 healthy control (HC) recordings, each of which includes 12-lead ECG and 3-lead VCG. Significant differences are found in the VCG spatial distribution between MI and HC groups. Furthermore, classification and regression tree (CART) analysis was used to demonstrate that VCG octant features can distinguish MIs from HCs with a sensitivity (accuracy of MI identification) of 97.28% and a specificity (accuracy of HC identification) of 95.00%, which is promising compared to the previously reported results using other ECG databases. The results indicate that the present approach provides an effective way for monitoring, post-processing, and interpretation of ECG data, and hopefully can impact the current cardiac diagnostic practice.     

Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8 % with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the cross-correlation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T₁- and T₂-weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1 % of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8 % with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate.     

Motion correction for three-dimensional chemical exchange saturation transfer imaging without direct water saturation artifacts

In chemical exchange saturation transfer (CEST) MRI, motion correction is compromised by the drastically changing image contrast at different frequency offsets, particularly at the direct water saturation. In this study, a simple extension for conventional image registration algorithms is proposed, enabling robust and accurate motion correction of CEST-MRI data. The proposed method uses weighted averaging of motion parameters from a conventional rigid image registration to identify and mitigate erroneously misaligned images. Functionality of the proposed method was verified by ground truth datasets generated from 10 three-dimensional in vivo measurements at 3 T with simulated realistic random rigid motion patterns and noise. Performance was assessed using two different criteria: the maximum image misalignment as a measure for the robustness against direct water saturation artifacts, and the spectral error as a measure of the overall accuracy. For both criteria, the proposed method achieved the best scores compared with two motion-correction algorithms specifically developed to handle the varying contrasts in CEST-MRI. Compared with a straightforward linear interpolation of the motion parameters at frequency offsets close to the direct water saturation, the proposed method offers better performance in the absence of artifacts. The proposed method for motion correction in CEST-MRI allows identification and mitigation of direct water saturation artifacts that occur with conventional image registration algorithms. The resulting improved robustness and accuracy enable reliable motion correction, which is particularly crucial for an automated and carefree evaluation of spectral CEST-MRI data, e.g., for large patient cohorts or in clinical routines.