A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

Goal: Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. Methods: Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. Results: IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. Conclusions: IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices.

A Database of Simultaneously Recorded ECG Signals With and Without EMG Noise

Goal: Noise on recorded electrocardiographic (ECG) signals may affect their clinical interpretation. Electromyographic (EMG) noise spectrally coincides with the QRS complex, which makes its removal particularly challenging. The problem of evaluating the noise-removal techniques has commonly been approached by algorithm testing on the contaminated ECG signals constructed ad hoc as an additive mixture of a noise-free ECG signal and noise. Consequently, there is an absence of a unique/standard database for testing and comparing different denoising methods. We present a SimEMG database recorded by a novel acquisition method that allows for direct recording of the genuine EMG-noise-free and -contaminated ECG signals. The database is available as open source.

Properties of different types of dry electrodes for wearable smart monitoring devices

Wearable smart monitors (WSMs) applied for the estimation of electrophysiological signals are of utmost interest for a non-stressed life. WSM which records heart muscle activities could signalize timely a life-threatening event. The heart muscle activities are typically recorded across the heart at the surface of the body; hence, a WSM monitor requires high-quality surface electrodes. The electrodes used in the clinical settings [i.e. silver/silver chloride (Ag/AgCl) with the gel] are not practical for the daily out of clinic usage. A practical WSM requires the application of a dry electrode with stable and reproducible electrical characteristics. We compared the characteristics of six types of dry electrodes and one gelled electrode during short-term recordings sessions (≈30 s) in real-life conditions: Orbital, monolithic polymer plated with Ag/AgCl, and five rectangular shaped 10 × 6 × 2 mm electrodes (Orbital, Ag electrode, Ag/AgCl electrode, gold electrode and stainless-steel AISI304). The results of a well-controlled analysis which considered motion artifacts, line noise and junction potentials suggest that among the dry electrodes Ag/AgCl performs the best. The Ag/AgCl electrode is in average three times better compared with the stainless-steel electrode often used in WSMs.

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design

OBJECTIVE

Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned.

METHODS

A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class.

RESULTS

The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators.

CONCLUSIONS

The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.

Real-time chest-wall-motion tracking by a single optical fibre grating: a prospective method for ventilator triggering

OBJECTIVE

The ventilators involved in non-invasive mechanical ventilation commonly provide ventilator support via a facemask. The interface of the mask with a patient promotes air leaks that cause errors in the feedback information provided by a pneumatic sensor and hence patient-ventilator asynchrony with multiple negative consequences. Our objective is to test the possibility of using chest-wall motion measured by an optical fibre-grating sensor as a more accurate non-invasive ventilator triggering mechanism.

APPROACH

The basic premise of our approach is that the measurement accuracy can be improved by using a triggering signal that precedes pneumatic triggering in the neuro-ventilatory coupling sequence. We propose a technique that uses the measurement of chest-wall curvature by a long-period fibre-grating sensor. The sensor was applied externally to the rib-cage and interrogated in the lateral (edge) filtering scheme. The study was performed on 34 healthy volunteers. Statistical data analysis of the time lag between the fibre-grating sensor and the reference pneumotachograph was preceded by the removal of the unwanted heartbeat signal by wavelet transform processing.

MAIN RESULTS

The results show a consistent fibre-grating signal advance with respect to the standard pneumatic signal by (230  ±  100) ms in both the inspiratory and expiratory phases. We further show that heart activity removal yields a tremendous improvement in sensor accuracy by reducing it from 60 ml to 0.3 ml.

SIGNIFICANCE

The results indicate that the proposed measurement technique may lead to a more reliable triggering decision. Its imperviousness to air leaks, non-invasiveness, low-cost and ease of implementation offer good prospects for applications in both clinical and homecare ventilation.

The synthesis and preliminary pharmacological evaluation of 4-methyl fentanyl

The synthesis of 4-methyl fentanyl, a prototype of a novel class of fentanyl analogues has been effected in 5 steps, starting from N-ethoxycarbonyl-4-piperidone (approximately 20% overall yield). In the key step, N-phenylation of secondary aliphatic amide intermediare was achieved by a novel reaction, using diphenyliodonium chloride for the phenyl group transfer. Preliminary pharmacological results indicate that 4-methyl fentanyl is a super potent narcotic analgesic, about four times more potent than fentanyl.