Enhancing the precision of ECG baseline correction: selective filtering and removal of residual error

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra

A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

A Statistical Approach of Background Removal and Spectrum Identification for SERS Data

SERS (surface-enhanced Raman scattering) enhances the Raman signals, but the plasmonic effects are sensitive to the chemical environment and the coupling between nanoparticles, resulting in large and variable backgrounds, which make signal matching and analyte identification highly challenging. Removing background is essential, but existing methods either cannot fit the strong fluctuation of the SERS spectrum or do not consider the spectra’s shape change across time. Here we present a new statistical approach named SABARSI that overcomes these difficulties by combining information from multiple spectra. Further, after efficiently removing the background, we have developed the first automatic method, as a part of SABARSI, for detecting signals of molecules and matching signals corresponding to identical molecules. The superior efficiency and reproducibility of SABARSI are shown on two types of experimental datasets.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV.

Fast time-varying linear filters for suppression of baseline drift in electrocardiographic signals

BACKGROUND

The paper presents a method of linear time-varying filtering, with extremely low computational costs, for the suppression of baseline drift in electrocardiographic (ECG) signals. An ECG signal is not periodic as the length of its heart cycles vary. In order to optimally suppress baseline drift by the use of a linear filter, we need a high-pass filter with time-varying cut-off frequency controlled by instant heart rate.

METHODS

Realization of the high-pass (HP) filter is based on a narrow-band low-pass (LP) filter of which output is subtracted from the delayed input. The base of an LP filter is an extremely low computational cost Lynn's filter with rectangular impulse response. The optimal cut-off frequency of an HP filter for baseline wander suppression is identical to an instantaneous heart rate. Instantaneous length of heart cycles (e.g. RR intervals) are interpolated between QRS complexes to smoothly control cut-off frequency of the HP filter that has been used.

RESULTS AND CONCLUSIONS

We proved that a 0.5 dB decrease in transfer function, at a time-varying cut-off frequency of HP filter controlled by an instant heart rate, is acceptable when related to maximum error due to filtering. Presented in the article are the algorithms that enable the realization of time-variable filters with very low computational costs. We propose fast linear HP filters for the suppression of baseline wander with time-varying cut-off frequencies controlled by instant heart rate. The filters fulfil accepted professional standards and increase the efficiency of the noise suppression.

Nighttime instabilities of neurophysiological, cardiovascular, and respiratory activity: integrative modeling and preliminary results

Unstable (cyclical alternating pattern, or CAP) sleep is associated with surges of sympathetic nervous system activity, increased blood pressure and vasoconstriction, heightened baroreflex sensitivity, and unstable heart rhythm and breathing. In susceptible persons, CAP sleep provokes clinically significant events, including hypertensive crises, sleep-disordered breathing, and cardiac arrhythmias. Here we explore the neurophysiology of CAP sleep and its impact on cardiovascular and respiratory functions. We show that: (i) an increase in neurophysiological recovery rate can explain the emergence of slow, self-sustained, hypersynchronized A1 CAP-sleep pattern and its transition to the faster A2-A3 CAP-sleep patterns; (ii) in a two-dimensional, continuous model of cardiac tissue with heterogeneous action potential duration (APD) distribution, heart rate accelerations during CAP sleep may encounter incompletely recovered electrical excitability in cell clusters with longer APD. If the interaction between short cycle length and incomplete, spatially heterogeneous repolarization persists over multiple cycles, irregularities and asymmetry of depolarization front may accumulate and ultimately lead to a conduction block, retrograde conduction, breakup of activation waves, reentrant activity, and arrhythmias; and (iii) these modeling results are consistent with the nighttime data obtained from patients with structural heart disease (N=13) that show clusters of atrial and ventricular premature beats occurring during the periods of unstable heart rhythm and respiration that accompany CAP sleep. In these patients, CAP sleep is also accompanied by delayed adaptation of QT intervals and T-wave alternans.

Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction

Raman spectra have been widely used in biology, physics, and chemistry and have become an essential tool for the studies of macromolecules. Nevertheless, the raw Raman signal is often obscured by a broad background curve (or baseline) due to the intrinsic fluorescence of the organic molecules, which leads to unpredictable negative effects in quantitative analysis of Raman spectra. Therefore, it is essential to correct this baseline before analyzing raw Raman spectra. Polynomial fitting has proven to be the most convenient and simplest method and has high accuracy. In polynomial fitting, the cost function used and its parameters are crucial. This article proposes a novel iterative algorithm named Goldindec, freely available for noncommercial use as noted in text, with a new cost function that not only conquers the influence of great peaks but also solves the problem of low correction accuracy when there is a high peak number. Goldindec automatically generates parameters from the raw data rather than by empirical choice, as in previous methods. Comparisons with other algorithms on the benchmark data show that Goldindec has a higher accuracy and computational efficiency, and is hardly affected by great peaks, peak number, and wavenumber.

Wavelet Transform-Based ECG Baseline Drift Removal for Body Surface Potential Mapping

This paper gives a new approach for the removal of slow baseline drift components of electrocardiographic (ECG) signals based on the discrete wavelet transform. The baseline drift is efficiently removed by zeroing the scaling coefficients of the discrete wavelet transform. Such approach can easily be combined with other wavelet based approaches for random noise reduction or power line interference reduction. The new pre-processing approach can remove the low-frequency components without introducing distortions in the ECG waveform.

Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction

Electrocardiographic (ECG) analysis plays an important ortant role in safety assessment during new drug development and in clinical diagnosis. The pre-processing of ECG analysis consists of low-frequency baseline wander (BW) correction and high-frequency artifact noise reduction from the raw ECG. We present approaches for BW correction and de-noising based on discrete wavelet transformation (DWT). We estimate the BW via coarse approximation in DWT with recommendations for how to select wavelets and the maximum depth for decomposition ition level. We reduce the high-frequency noise via Empirical Bayes posterior median wavelet shrinkage method with leveldependent ependent and position dependent thresholding values. The methods are applied to a real example. The experimental results indicate that the proposed method can effectively remove both low-and high-frequency noise.

Cardiac repolarization instability during psychological stress in patients with ventricular arrhythmias

INTRODUCTION

Changes in the autonomic nervous system activity are a major trigger of life-threatening ventricular tachyarrhythmias (VTAs). Mental arithmetic, a condition administered in a laboratory setting, can provide insight into the autonomic nervous system activity effects on cardiac physiology. We examined the responses of cardiac repolarization to laboratory-induced psychological stressors in patients with implantable cardioverter-defibrillators (ICDs) with the objective of identifying the indices that differentiate patients with and without subsequent VTA in follow-up.

METHODS

Continuous electrocardiographic signals were recorded using 3 standard bipolar (Holter) leads in 56 patients (age, 63.6 ± 11.9; female, 12%; left ventricular ejection fraction, 32.3 ± 11) with ICDs during mental arithmetic. The patients were separated into those with subsequent VTA during 3 to 4 years of follow-up (group 1: n = 9) and those without VTA (group 2: n = 47). Changes in repolarization (QT interval, mean T wave amplitude [Tamp], and T wave area) were analyzed during 5 minutes at baseline, stress, and recovery. The temporal instability of Tamp and T wave area was examined using the range (Δ) and variance (σ(2)) of beat-to-beat variations of the corresponding parameters.

RESULTS

There were no significant differences in heart rate between the 2 groups at baseline (61 vs 63 beats per minute, P = .97), stress (64 vs 65 beats per minute, P = .40), and recovery (62 vs 61 beats per minute, P = .88). However, during mental stress and poststress recovery, ΔTamp was almost 2-fold greater in group 1 compared with group 2 (111 [57-203] vs 68 [44-94] μV, P = .04, respectively). Changes in QT intervals were also greater in group 1 compared with group 2 (P = .02).

CONCLUSION

Among patients with ICDs, changes of Tamp after psychological stress were greater in those with subsequent arrhythmic events. This might signal proarrhythmic repolarization response and help identify patients who would benefit the most from ICD implantation and proactive management.

Adrenergic stimulation promotes T-wave alternans and arrhythmia inducibility in a TNF-alpha genetic mouse model of congestive heart failure

T-wave alternans (TWA) is a proarrhythmic repolarization instability that is common in congestive heart failure (CHF). Although transgenic mice are commonly used to study the mechanisms of arrhythmogenesis in CHF, little is known about the dynamics of TWA in these species. We hypothesized that TWA is present in a TNF-alpha model of CHF and can be further promoted by adrenergic stimulation. We studied 16 TNF-alpha mice and 12 FVB controls using 1) in vivo intracardiac electrophysiological testing and 2) ambulatory telemetry during 30 min before and after an intraperitoneal injection of isoproterenol. TWA was examined using both linear and nonlinear filtering applied in the time domain. In addition, changes in the mean amplitude of the T wave and area under the T wave were computed. During intracardiac electrophysiological testing, none of the animals had TWA or inducible arrhythmias before the injection of isoproterenol. After the injection, sustained TWA and inducible ventricular tachyarrhythmias were observed in TNF-alpha mice but not in FVB mice. In ambulatory telemetry, before the isoproterenol injection, the cardiac cycle length (CL) was longer in TNF-alpha mice than in FVB mice (98 +/- 9 and 88 +/- 3 ms, P = 0.04). After the injection of isoproterenol, the CL became 8% and 6% shorter in TNF-alpha and FVB mice (P < 10(-4)); however, the 2% difference between the groups in the magnitude of CL changes was not significant. In TNF-alpha mice, the magnitude of TWA was 1.5-2 times greater than in FVB mice both before and after the isoproterenol injection. The magnitude of TWA increased significantly after the isoproterenol injection compared with the baseline in TNF-alpha mice (P = 0.003) but not in FVB mice. The mean amplitude of the T wave and area under the T wave increased 60% and 80% in FVB mice (P = 0.006 and 0.009) but not in TNF-alpha mice. In conclusion, TWA is present in a TNF-alpha model of CHF and can be further promoted by adrenergic stimulation, along with the enhanced susceptibility for ventricular arrhythmias.

Machine Learning: A Crucial Tool for Sensor Design

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

Improving ECG beats delineation with an evolutionary optimization process

As in other complex signal processing tasks, ECG beat delineation algorithms are usually constituted of a set of processing modules, each one characterized by a certain number of parameters (filter cutoff frequencies, threshold levels, time windows, etc.). It is well recognized that the adjustment of these parameters is a complex task that is traditionally performed empirically and manually, based on the experience of the designer. In this paper, we propose a new automated and quantitative method to optimize the parameters of such complex signal processing algorithms. To solve this multiobjective optimization problem, an evolutionary algorithm (EA) is proposed. This method for parameter optimization is applied to a wavelet-transform-based ECG delineator that has previously shown interesting performance. An evaluation of the final delineator, using the optimal parameters, has been performed on the QT database from Physionet and results are compared with previous algorithms reported in the literature. The optimized parameters provide a more accurate delineation, with a global improvement of 7.7%, over all the criteria evaluated, and over the best results found in the literature, which is a proof of interest in the approach.

Dynamic tracking of ischemia in the surface electrocardiogram

BACKGROUND

Accurate detection of the earliest signs of ischemia on the surface electrocardiogram (ECG) is essential for timely diagnosis and management of potentially life-threatening ischemic events. Yet, accuracy of ischemia analysis in ECG monitors remains suboptimal because of a number of confounding factors, including changes in body position and other artifacts. Hence, the goals of this study were (1) to examine the duration and time course of ischemic events and (2) to compare ECG changes caused by "true" ischemic events with those caused by changes in body position. Continuous, 12-lead Holter ECGs obtained from patients who presented to the emergency department with chest pain and enrolled in the Ischemia Monitoring and Mapping in the Emergency Department in Appropriate Triage and Evaluation of Acute Ischemic Myocardium study were analyzed. Holter recordings were initiated within the first 40 minutes after patients' arrival to the emergency department. Here we present preliminary results.

METHODS

Twelve patients (age, 59 +/- 16 years; 5 women, 2 with a final diagnosis of non-ST-segment elevation myocardial infarction, 4 with unstable angina, and 6 with other cardiovascular diseases), in whom ischemic ST deviations were identified on Holter data, underwent 4 consecutive, 2-minute recordings in the following body positions: (1) supine, (2) on the left side, (3) on the right side, and (4) sitting (or standing) upright. After baseline correction, beat-to-beat changes in QRS and ST-T segments were examined in all 8 channels and the root-mean-square curve by using an adaptive algorithm that computes the slope, amplitude, duration, area, and the Karhunen-Loève-derived representation of the corresponding segment. To prevent possible biases toward patients with more frequent ischemic events, a single index event was chosen for analysis in each patient. There were 3 ST-elevation events and 9 ST-depression events; these events reached the maximum ST deviation 11 +/- 8 hours (mean +/- SD) after the beginning of the recording.

RESULTS AND CONCLUSIONS

In most patients with transient myocardial ischemia, the microvolt-level, subthreshold deviation of the ST segment developed gradually, over 15 to 20 minutes, until it reached the maximum, superthreshold level. Despite the different ischemia localizations, the root-mean-square curve allowed accurate detection of significant changes in the ST segment in the studied group (Friedman analysis of variance for repeated measurements over a 1-hour interval). Changes in body position could be identified by tracking dynamics of the QRS pattern/axis. Adaptive algorithms for tracking of the ST dynamics with simultaneous tracking of the patterns of QRS complexes to discriminate the true and "false"-positive events are presented and discussed.

Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter

Pulse diagnosis is a convenient, inexpensive, painless, and non-invasive diagnosis method. Quantifying pulse diagnosis is to acquire and record pulse waveforms by a set of sensor firstly, and then analyze these pulse waveforms. However, respiration and artifact motion during pulse waveform acquisition can introduce baseline wander. It is necessary, therefore, to remove the pulse waveform's baseline wander in order to perform accurate pulse waveform analysis. This paper presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline wander of pulse waveform. To evaluate the level of baseline wander, we introduce a criterion: energy ratio (ER) of pulse waveform to its baseline wander. If the ER is more than a given threshold, the baseline wander can be removed only by cubic spline estimation; otherwise it must be filtered by, in sequence, discrete Meyer wavelet filter and the cubic spline estimation. Compared with traditional methods such as cubic spline estimation, morphology filter and Linear-phase finite impulse response (FIR) least-squares-error digital filter, the experimental results on 50 simulated and 500 real pulse signals demonstrate the power of CAF filter both in removing baseline wander and in preserving the diagnostic information of pulse waveforms. This CAF filter also can be used to remove the baseline wander of other physiological signals, such as ECG and so on.

Upsurge in T-Wave Alternans and Nonalternating Repolarization Instability Precedes Spontaneous Initiation of Ventricular Tachyarrhythmias in Humans

Background— Analysis of repolarization instability, manifested by T-wave alternans (TWA), has proved useful for arrhythmia risk assessment. However, temporal relations between TWA and the spontaneous initiation of ventricular tachyarrhythmias (VTA) in humans are unknown. We examined continuous dynamics of repolarization in Holter electrocardiograms with spontaneous sustained (>30 seconds) VTA.

Methods and Results— Ambulatory electrocardiograms from 42 patients (79% with ischemic heart disease; left ventricular ejection fraction, 37±15%) were digitized, and the lead with the highest magnitude of the T wave was selected for analysis. TWA was examined by the modified moving average and intrabeat average analyses. To examine non-TWA (longer-period) oscillations in the repolarization segment, spectral energy of oscillations of consecutive T-wave amplitudes was calculated with the use of the short-time Fourier transform. Heart rate variability was assessed with the Fourier transform as well. TWA increased before the onset of VTA and reached a peak value of 23.6±11.7 μV 10 minutes before the event ( P =0.0007). Spectral power of the oscillations of consecutive T-wave amplitudes increased nonuniformly, with the greatest increase in the respiratory range (2.6 μV 2 ; P =0.005). In the TWA range, the change was smaller but highly pronounced relative to the 60- to 120-minute level (65%; P =0.003). The low-frequency and high-frequency heart rate variability power declined before the arrhythmia ( P =0.04 and P =0.06, respectively).

Conclusions— The magnitude of repolarization instability, manifested by TWA and beat-to-beat oscillations of T-wave amplitudes at other frequencies, increased before the onset of VTA. Tracking of these dynamics can facilitate timely detection of high-risk periods and may be useful for initiation of preventive treatments.

Bluetooth telemedicine processor for multichannel biomedical signal transmission via mobile cellular networks

One of the emerging issues in m-Health is how best to exploit the mobile communications technologies that are now almost globally available. The challenge is to produce a system to transmit a patient's biomedical signals directly to a hospital for monitoring or diagnosis, using an unmodified mobile telephone. The paper focuses on the design of a processor, which samples signals from sensors on the patient. It then transmits digital data over a Bluetooth link to a mobile telephone that uses the General Packet Radio Service. The modular design adopted is intended to provide a "future-proofed" system, whose functionality may be upgraded by modifying the software.

The triangular wave test for electrocardiographic devices: a historical perspective

Baseline wander makes interpretation ECG recordings difficult, especially the assessment of ST deviation. Eliminating baseline wander without distorting the ST segment is a problem. The traditional high pass filter with a 0.5 Hz low frequency cutoff effectively suppresses baseline but introduces considerable distortion in the level of the ST segment. This distortion results from phase nonlinearities that occur when frequency content and wave amplitude change abruptly, as occurs where the end of the QRS complex meets the ST segment. Since the 1980s nonlinear, digital filters have been designed that can increase the low frequency cutoff without the introduction of phase distortion. The triangular wave test, first described in the 1990 AHA Recommendations, is an objective method for measuring the ability to suppress baseline wander without affecting the ST segment. This methodology was adopted in 3 American National Standards.

Tracking repolarization dynamics in real-life data

Ambulatory (Holter) electrocardiographic recordings provide the tools for tracking temporal instabilities of repolarization during various daily activities. However, analysis of low-amplitude repolarization changes in this setting is challenging due to the presence of multiple artifacts, variable activity levels, and other uncontrolled factors. Here we compare performance of different methods for continuous analysis of repolarization dynamics using simulated signals and real-life Holter recordings. Selection of relatively stable segments with a low baseline drift and accurate correction of baseline wander constitute the first step in repolarization analysis. We describe application of adaptive filtering, which yields more accurate results than non-adaptive techniques. Because small (microvolt-level) residual baseline drifts can be a source of error in tracking repolarization changes, stability of isoelectrical segment has to be controlled. To compare robustness of spectral and time-domain techniques for tracking temporal repolarization instabilities (T-wave alternans, TWA), we used simulated signals with changing heart rate, variable levels of TWA, noise, phase shifts, spurious artifacts, and period-four oscillations. In addition, we compared performances of the inter-beat and intra-beat averaging techniques for tracking dynamics of T-wave alternans. Using the simulated signals and real-life Holter data, we showed that analysis of information both in time and frequency domains combined with control of baseline drifts (surrogate analysis) gives a more reliable estimate of the low-amplitude repolarization dynamics than each of these techniques alone. To summarize, dynamic tracking of low-amplitude repolarization changes in ambulatory recordings is possible during most of the recording time but requires accurate control of baseline wander and stability of isoelectrical segments. Analysis of time-frequency distributions embedded in repolarization dynamics facilitates detection of abrupt and transient repolarization instabilities, including changes in the level of T-wave alternans and slower periodicities.