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Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Awais M, Badruddin N, Drieberg M

Sensors (Basel), 2017 Aug 31;17(9).
PMID: 28858220 DOI: 10.3390/s17091991

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

Matched MeSH terms: Electrocardiography
Fulltext A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Ullah A, Rehman SU, Tu S, Mehmood RM, Fawad, Ehatisham-Ul-Haq M

Sensors (Basel), 2021 Feb 01;21(3).
PMID: 33535397 DOI: 10.3390/s21030951

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.

Matched MeSH terms: Electrocardiography*
Fulltext A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Mahmud S, Ibtehaz N, Khandakar A, Tahir AM, Rahman T, Islam KR, et al.

Sensors (Basel), 2022 Jan 25;22(3).
PMID: 35161664 DOI: 10.3390/s22030919

Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.

Matched MeSH terms: Electrocardiography
Fulltext A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Wu M, Lu Y, Yang W, Wong SY

Front Comput Neurosci, 2020;14:564015.
PMID: 33469423 DOI: 10.3389/fncom.2020.564015

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

Matched MeSH terms: Electrocardiography
A case of situs inversus

Banu SZ

Med J Malaysia, 1977 Mar;31(3):236-40.
PMID: 904519
Matched MeSH terms: Electrocardiography
Fulltext A comparison of enoxaparin with unfractionated heparins in patients with coronary heart disease in an emergency department in rural South Indian tertiary care teaching hospital

Ahmad A, Patel I, Asani H, Jagadeesan M, Parimalakrishnan S, Selvamuthukumaran S

Indian J Pharmacol, 2015 Jan-Feb;47(1):90-4.
PMID: 25821318 DOI: 10.4103/0253-7613.150360

Antithrombotic therapy with heparin plus antiplatelets reduces the rate of ischemic events in patients with coronary heart disease. Low molecular weight heparin has a more predictable anticoagulant effect than standard unfractionated heparin, is easier to administer, does not require monitoring and is associated with less ADRs. The purpose of the present study was to evaluate and compare the clinical and cost outcomes of Enoxaparin with a standard unfractionated heparin in patients with coronary heart disease.

Matched MeSH terms: Electrocardiography
Fulltext A comparison of long-term outcomes between narrow and broad QRS complex patients treated with cardiac resynchronization therapy

Yap LB, Nguyen ST, Qadir F, Ma SK, Muhammad Z, Koh KW, et al.

Acta Cardiol, 2016 Jun;71(3):323-30.
PMID: 27594128 DOI: 10.2143/AC.71.3.3152093
Matched MeSH terms: Electrocardiography/methods*
Fulltext A comparison study of radiation effective dose in ECG-Gated Coronary CT Angiography and calcium scoring examinations performed with a dual-source CT scanner

Sabarudin A, Siong TW, Chin AW, Hoong NK, Karim MKA

Sci Rep, 2019 03 13;9(1):4374.
PMID: 30867480 DOI: 10.1038/s41598-019-40758-5

In this report we have evaluated radiation effective dose received by patients during ECG-gated CCTA examinations based on gender, heart rate, tube voltage protocol and body mass index (BMI). A total of 1,824 patients were retrospectively recruited (1,139 men and 685 women) and they were divided into Group 1 (CCTA with calcium scoring), Group 2 (CCTA without calcium scoring) and Group 3 (only calcium scoring), where the association between gender, heart rate, tube voltage protocol and body mass index (BMI) were analysed. Examinations were performed using a retrospective ECG-gated CCTA protocol and the effective doses were calculated from the dose length product with a conversion coefficient of 0.026 mSv.mGy-1cm-1. No significant differences were observed in the mean effective dose between gender in all groups. The mean estimated dose was significantly higher when the heart rate was lower in Group 1 (p

Matched MeSH terms: Electrocardiography*
A deep convolutional neural network model to classify heartbeats

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al.

Comput Biol Med, 2017 10 01;89:389-396.
PMID: 28869899 DOI: 10.1016/j.compbiomed.2017.08.022

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.

Matched MeSH terms: Electrocardiography*
A new approach for arrhythmia classification using deep coded features and LSTM networks

Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2019 Jul;176:121-133.
PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004

BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

Matched MeSH terms: Electrocardiography/methods*; Electrocardiography, Ambulatory
A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank

Sharma M, Tan RS, Acharya UR

Comput Biol Med, 2018 11 01;102:341-356.
PMID: 30049414 DOI: 10.1016/j.compbiomed.2018.07.005

Myocardial infarction (MI), also referred to as heart attack, occurs when there is an interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic plaque, which leads to damage of heart muscle. The heart muscle damage produces changes in the recorded surface electrocardiogram (ECG). The identification of MI by visual inspection of the ECG requires expert interpretation, and is difficult as the ECG signal changes associated with MI can be short in duration and low in magnitude. Hence, errors in diagnosis can lead to delay the initiation of appropriate medical treatment. To lessen the burden on doctors, an automated ECG based system can be installed in hospitals to help identify MI changes on ECG. In the proposed study, we develop a single-channel single lead ECG based MI diagnostic system validated using noisy and clean datasets. The raw ECG signals are taken from the Physikalisch-Technische Bundesanstalt database. We design a novel two-band optimal biorthogonal filter bank (FB) for analysis of the ECG signals. We present a method to design a novel class of two-band optimal biorthogonal FB in which not only the product filter but the analysis lowpass filter is also a halfband filter. The filter design problem has been composed as a constrained convex optimization problem in which the objective function is a convex combination of multiple quadratic functions and the regularity and perfect reconstruction conditions are imposed in the form linear equalities. ECG signals are decomposed into six subbands (SBs) using the newly designed wavelet FB. Following to this, discriminating features namely, fuzzy entropy (FE), signal-fractal-dimensions (SFD), and renyi entropy (RE) are computed from all the six SBs. The features are fed to the k-nearest neighbor (KNN). The proposed system yields an accuracy of 99.62% for the noisy dataset and an accuracy of 99.74% for the clean dataset, using 10-fold cross validation (CV) technique. Our MI identification system is robust and highly accurate. It can thus be installed in clinics for detecting MI.

Matched MeSH terms: Electrocardiography
A puzzling case of cardiac arrest

Hannah HB

Br J Anaesth, 1971 Oct;43(10):991-3.
PMID: 5115036
Matched MeSH terms: Electrocardiography
Fulltext A qualitative study on negative attitude toward electrocardiogram learning among undergraduate medical students

Ohn MH, Souza U, Ohn KM

Tzu Chi Med J, 2020 08 02;32(4):392-397.
PMID: 33163387 DOI: 10.4103/tcmj.tcmj_91_19

Objective: Negative affect state toward learning has a substantial impact on the learning process, academic performance, and practice of a particular subject, but such attitude toward electrocardiogram (ECG) learning has still received relatively little attention in medical education research. In spite of the significant emphasis in investigating ECG teaching method, the educators would not be able to address ECG incompetency without understanding the negative perception and attitude toward ECG learning. The purpose of this study was to assess the undergraduate students' difficulties in ECG learning and hence help educators design appropriate ECG learning curriculum to instill competent skill in ECG interpretation based on this outcome.
Materials and Methods: A total of 324 undergraduate preclinical (year 2) and clinical (year 3-5) medical students participated in this study. The research design used thematic analysis of an open-ended questionnaire to analyze the qualitative data.
Results: The thematic analysis detected five major emergent themes: lack of remembering (18.2%), lack of understanding (28.4%), difficulty in applying (3.6%), difficulty in analysis (15.1%), and difficulty in interpretation (17.8%), of which addressing these challenges could be taken as a foundation step upon which medical educators put an emphasis on in order to improve ECG teaching and learning.
Conclusion: Negative attitude toward ECG learning poses a serious threat to acquire competency in ECG interpretation skill. The concept of student's memorizing ECG is not a correct approach; instead, understanding the concept and vector analysis is an elementary key for mastering ECG interpretation skill. The finding of this study sheds light into a better understanding of medical students' deficient points of ECG learning in parallel with taxonomy of cognitive domain and enables the medical teachers to come up with effective and innovative strategies for innovative ECG learning in an undergraduate medical curriculum.

Matched MeSH terms: Electrocardiography
Fulltext A rare case of Riamet induced prolonged corrected QT interval

Neesha Sundramoorthy, Khaiteri R., Jer Ming Low, Chan Soon Thim Darren

Malaysian Journal of Medicine and Health Sciences, 2019;15(106):70-70.
MyJurnal

Introduction: Artemether and lumefantrine was registered as Riamet in Switzerland in 1999 and is commonly used in Keningau Hospital for managing uncomplicated malaria. Riamet works at the food vacoule of the malarial parasite, where they interfere with the conversion of heme into haemozoin. Case description: We report a case of Riamet induced prolonged corrected QT interval (QTc) in a 37 year old gentleman admitted for severe malaria (hypotension) with normal QTc of 420msc on presentation. Upon starting Riamet, he developed bradycardia and ECG showed sinus bradycardia with prolonged QTc of 551msec and no arrythmias. Echocardiography showed no structural heart abnormalities. All electrolytes were within normal range. He was monitored in cardiac care unit with decision to complete 6 doses of Riamet. Patient was started on Dopamine infusion which maintained his blood pressure and heart rate within normal range. 5 days post Riamet completion, his heart rate improved and dopamine infusion was tapered off and QTc normalized to 407msc. Discussion: The most common mechanism of drugs causing QT inter-val prolongation is by blocking the human ether-à-go-go related gene (hERG) potassium channel. Blockage of the hEGR channel lengthens ventricular re-polarization and duration of ventricular action potential which is reflected in ECG as prolonged QT interval. In the in-vitro whole cell patch clamp study, lumefantrine and its metabolite desbu-tyl-lumefantrine showed a concentration-dependent inhibition of the hERG current. The period of QTc prolongation was 3.5 to 4 days after the last dose of the standard 6 dose regimen. Conclusion: Riamet induced prolonged QTc is a very rare complication. A baseline electrocardiography is therefore imminent for every patient prior to initiation of this medication to avoid cardiac arrythmias.

Matched MeSH terms: Electrocardiography
A review of automated sleep stage scoring based on physiological signals for the new millennia

Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2019 Jul;176:81-91.
PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032

BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

Matched MeSH terms: Electrocardiography
Fulltext A young patient with history of Kawasaki disease presenting with triple vessel disease

Lim AL, Lam HY, Kareem BA, Kamarulzaman MH

Med J Malaysia, 2012 Apr;67(2):219-21.
PMID: 22822650 MyJurnal

Kawasaki disease is primarily a condition that affects young children and it is associated with cardiac morbidity and mortality. This disease has been known to cause coronary artery aneurysms which occurs as a sequelae of vasculitis. The progression of triple vessel disease in adult which results from cardiac complications from Kawasaki disease is rare. We report a case of a young man with history of Kawasaki disease at infancy presenting with triple vessel disease requiring cardiac bypass surgery at the age of 20 years old.

Matched MeSH terms: Electrocardiography
Fulltext Accelerometer-based atrioventricular synchronous pacing with a ventricular leadless pacemaker: Results from the Micra atrioventricular feasibility studies

Chinitz L, Ritter P, Khelae SK, Iacopino S, Garweg C, Grazia-Bongiorni M, et al.

Heart Rhythm, 2018 09;15(9):1363-1371.
PMID: 29758405 DOI: 10.1016/j.hrthm.2018.05.004

BACKGROUND: Micra is a leadless pacemaker that is implanted in the right ventricle and provides rate response via a 3-axis accelerometer (ACC). Custom software was developed to detect atrial contraction using the ACC enabling atrioventricular (AV) synchronous pacing.
OBJECTIVE: The purpose of this study was to sense atrial contractions from the Micra ACC signal and provide AV synchronous pacing.
METHODS: The Micra Accelerometer Sensor Sub-Study (MASS) and MASS2 early feasibility studies showed intracardiac accelerations related to atrial contraction can be measured via ACC in the Micra leadless pacemaker. The Micra Atrial TRacking Using A Ventricular AccELerometer (MARVEL) study was a prospective multicenter study designed to characterize the closed-loop performance of an AV synchronous algorithm downloaded into previously implanted Micra devices. Atrioventricular synchrony (AVS) was measured during 30 minutes of rest and during VVI pacing. AVS was defined as a P wave visible on surface ECG followed by a ventricular event <300 ms.
RESULTS: A total of 64 patients completed the MARVEL study procedure at 12 centers in 9 countries. Patients were implanted with a Micra for a median of 6.0 months (range 0-41.4). High-degree AV block was present in 33 patients, whereas 31 had predominantly intrinsic conduction during the study. Average AVS during AV algorithm pacing was 87.0% (95% confidence interval 81.8%-90.9%), 80.0% in high-degree block patients and 94.4% in patients with intrinsic conduction. AVS was significantly greater (P

Matched MeSH terms: Electrocardiography/methods*
Acute myocarditis mimicking ST-elevation myocardial infarction: A diagnostic challenge for frontline clinicians

Ang KP, Quek ZQ, Lee CY, Lu HT

Med J Malaysia, 2019 12;74(6):561-563.
PMID: 31929492

The clinical presentation of acute myocarditis is highly variable ranging from no symptoms to cardiogenic shock. Despite considerable progress, it remains a challenge for frontline physicians to discriminate between acute myocarditis and myocardial infarction, especially in the early phase. Our case serves as a reminder that acute presentation of myocarditis could resemble ST elevation myocardial infarction potentially misdirecting the therapeutic decision. The clinical presentation, electrocardiographic and laboratory findings of the patient are not specific enough to distinguish acute myocarditis from myocardial infarction. The gold standard tests such coronary angiography and cardiovascular magnetic resonance (CMR) can reliably differentiate the two entities.

Matched MeSH terms: Electrocardiography
Fulltext Acute pancreatitis with electrocardiographic changes mimicking acute coronary syndrome: a diagnostic conundrum

Shamala N., Faizal, A.H.

Medicine & Health, 2018;13(2):195-201.
MyJurnal

Electrocardiographic abnormalities can be associated with acute pancreatitis. However, data regarding the actual causative factor still remains elusive. Many previous cases were reported on non-specific ST and T wave abnormalities concurrent with acute pancreatitis but rarely with an increasing trend of cardiac markers. We describe the case of a 70-year-old female who presented with one such conundrum. Our patient had typical presentation of acute pancreatitis but had dynamic ECG changes with markedly increased cardiac markers. Subsequently after initiation of treatment for acute pancreatitis and observation for the course of several days, the ECG returned to the baseline as pre admission. This substantiates the fact that acute pancreatitis can mimic both biochemical and electrical manifestation of an acute coronary syndrome. Thus, Emergency Physicians should consider acute pancreatitis as a possible diagnosis in patients who present with abnormal electrocardiograms.

Matched MeSH terms: Electrocardiography
Fulltext Acute viral myocarditis, four cases in three weeks

Sufarlan AW, Khalid BA

Med J Malaysia, 1989 Dec;44(4):334-40.
PMID: 2520044

Four cases of acute viral myocarditis were diagnosed within three weeks. The clinical features, electrocardiography, cardiac enzymes and other laboratory investigations are described.

Matched MeSH terms: Electrocardiography

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