Using Lempel-Ziv Complexity to Assess ECG Signal Quality

Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

Background

The current clinical practice to diagnose atrial fibrillation (AF) requires repeated episodic monitoring and significantly underperform in their ability to detect AF episodes.

Objective

There is therefore potential for artificial intelligence-based methods to assist in the detection of AF. Better understanding of the optimal parameters for this detection can potentially improve the sensitivity for detecting AF.

Methods

Ten-second, 12-lead electrocardiogram signals were analyzed using complexity algorithms combined with machine learning techniques to predict patients who had a previously detected AF episode but had since returned to normal sinus rhythm. An investigation was performed into the impact of the sampling frequency of the electrocardiogram signal on the accuracy of the machine learning models used.

Results

Using a single complexity algorithm showed a peak accuracy of 0.69 when using signals sampled at 125 Hz. In particular, it was noted that improved accuracy occurred when using lead V6 compared with other available leads.

Conclusion

Based on these results, there is potential for 12-lead electrocardiogram signals to be recorded at 125 Hz as standard and used in conjunction with complexity analysis to aid in the detection of patients with AF.

MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus (p < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.

A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals

Objective. Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features.Approach. Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and 'UNSW' R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database.Main results. For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% in the synthesized datasets with a signal-to-noise ratio of 6 dB. With the same datasets, the Se of the FB algorithm increased about 9.29%, and a 3.64% increase occurred in the Se of the 'UNSW' algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% in the synthesized one-heartbeat datasets. It is the first study to utilize a DNN to capture noisy segments of the ECG signal.Significance. Too many false alarms can cause alarm fatigue. Our method can be utilized as the preprocessing before signal analysis, thereby reducing false alarms from the ECG diagnostic systems.

Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study

Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.

Computational approaches for detection of cardiac rhythm abnormalities: Are we there yet?

The analysis of an electrocardiogram (ECG) is able to provide vital information on the electrical activity of the heart and is crucial for the accurate diagnosis of cardiac arrhythmias. Due to the nature of some arrhythmias, this might be a time-consuming and difficult to accomplish process. The advent of novel machine learning technologies in this field has a potential to revolutionise the use of the ECG. In this review, we outline key advances in ECG analysis for atrial, ventricular and complex multiform arrhythmias, as well as discuss the current limitations of the technology and the barriers that must be overcome before clinical integration is feasible.

Using Lempel-Ziv complexity as effective classification tool of the sleep-related breathing disorders

BACKGROUND AND OBJECTIVE

People suffer from sleep disorders caused by work-related stress, irregular lifestyle or mental health problems. Therefore, development of effective tools to diagnose sleep disorders is important. Recently, to analyze biomedical signals Information Theory is exploited. We propose efficient classification method of sleep anomalies by applying entropy estimating algorithms to encoded ECGs signals coming from patients suffering from Sleep-Related Breathing Disorders (SRBD).

METHODS

First, ECGs were discretized using the encoding method which captures the biosignals variability. It takes into account oscillations of ECG measurements around signals averages. Next, to estimate entropy of encoded signals Lempel-Ziv complexity algorithm (LZ) which measures patterns generation rate was applied. Then, optimal encoding parameters, which allow distinguishing normal versus abnormal events during sleep with high sensitivity and specificity were determined numerically. Simultaneously, subjects' states were identified using acoustic signal of breathing recorded in the same period during sleep.

RESULTS

Random sequences show normalized LZ close to 1 while for more regular sequences it is closer to 0. Our calculations show that SRBDs have normalized LZ around 0.32 (on average), while control group has complexity around 0.85. The results obtained to public database are similar, i.e. LZ for SRBDs around 0.48 and for control group 0.7. These show that signals within the control group are more random whereas for the SRBD group ECGs are more deterministic. This finding remained valid for both signals acquired during the whole duration of experiment, and when shorter time intervals were considered. Proposed classifier provided sleep disorders diagnostics with a sensitivity of 93.75 and specificity of 73.00%. To validate our method we have considered also different variants as a training and as testing sets. In all cases, the optimal encoding parameter, sensitivity and specificity values were similar to our results above.

CONCLUSIONS

Our pilot study suggests that LZ based algorithm could be used as a clinical tool to classify sleep disorders since the LZ complexities for SRBD positives versus healthy individuals show a significant difference. Moreover, normalized LZ complexity changes are related to the snoring level. This study also indicates that LZ technique is able to detect sleep abnormalities in early disorders stage.

The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings

The analysis of equine electrocardiographic (ECG) recordings is complicated by the absence of agreed abnormality classification criteria. We explore the applicability of several complexity analysis methods for characterization of non-linear aspects of electrocardiographic recordings. We here show that complexity estimates provided by Lempel-Ziv ’76, Titchener’s T-complexity and Lempel-Ziv ’78 analysis of ECG recordings of healthy Thoroughbred horses are highly dependent on the duration of analysed ECG fragments and the heart rate. The results provide a methodological basis and a feasible reference point for the complexity analysis of equine telemetric ECG recordings that might be applied to automate detection of equine arrhythmias in equine clinical practice.

Complexity Analysis of Global Temperature Time Series

Climate has complex dynamics due to the plethora of phenomena underlying its evolution. These characteristics pose challenges to conducting solid quantitative analysis and reaching assertive conclusions. In this paper, the global temperature time series (TTS) is viewed as a manifestation of the climate evolution, and its complexity is calculated by means of four different indices, namely the Lempel–Ziv complexity, sample entropy, signal harmonics power ratio, and fractal dimension. In the first phase, the monthly mean TTS is pre-processed by means of empirical mode decomposition, and the TTS trend is calculated. In the second phase, the complexity of the detrended signals is estimated. The four indices capture distinct features of the TTS dynamics in a 4-dim space. Hierarchical clustering is adopted for dimensional reduction and visualization in the 2-dim space. The results show that TTS complexity exhibits space-time variability, suggesting the presence of distinct climate forcing processes in both dimensions. Numerical examples with real-world data demonstrate the effectiveness of the approach.

Grid mapping: a novel method of signal quality evaluation on a single lead electrocardiogram

Diagnosis of long-term electrocardiogram (ECG) calls for automatic and accurate methods of ECG signal quality estimation, not only to lighten the burden of the doctors but also to avoid misdiagnoses. In this paper, a novel waveform-based method of phase-space reconstruction for signal quality estimation on a single lead ECG was proposed by projecting the amplitude of the ECG and its first order difference into grid cells. The waveform of a single lead ECG was divided into non-overlapping episodes (T_s = 10, 20, 30 s), and the number of grids in both the width and the height of each map are in the range [20, 100] (N_X = N_Y = 20, 30, 40, … 90, 100). The blank pane ratio (BPR) and the entropy were calculated from the distribution of ECG sampling points which were projected into the grid cells. Signal Quality Indices (SQI) bSQI and eSQI were calculated according to the BPR and the entropy, respectively. The MIT-BIH Noise Stress Test Database was used to test the performance of bSQI and eSQI on ECG signal quality estimation. The signal-to-noise ratio (SNR) during the noisy segments of the ECG records in the database is 24, 18, 12, 6, 0 and - 6 dB, respectively. For the SQI quantitative analysis, the records were divided into three groups: good quality group (24, 18 dB), moderate group (12, 6 dB) and bad quality group (0, - 6 dB). The classification among good quality group, moderate quality group and bad quality group were made by linear support-vector machine with the combination of the BPR, the entropy, the bSQI and the eSQI. The classification accuracy was 82.4% and the Cohen's Kappa coefficient was 0.74 on a scale of N_X = 40 and T_s = 20 s. In conclusion, the novel grid mapping offers an intuitive and simple approach to achieving signal quality estimation on a single lead ECG.