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The QRS, ST segment, and T-wave waveforms of electrocardiogram are difficult to interpret, especially for non-ECG experts readers, like general practitioners. As the ECG waveforms are influenced by many factors, like body build, age, sex, electrode placement, even for experience ECG readers the waveform is difficult to interpret. In this research we have created a novel method to distinguish normal from abnormal ECG waveforms for an individual ECG based on the ECG amplitude distribution derived from normal standard 12‑lead ECG recordings.Creation of a normal ECG amplitude distribution to enable the distinction by non-ECG experts of normal from abnormal waveforms of the standard 12‑lead ECG.The ECGs of healthy normal controls in the PTB-XL database were used to construct a normal amplitude distribution of the 12 lead ECG for males and females. All ECGs were resampled to have the same number of samples to enable the classification of an individual ECG as either normal or abnormal, i.e. within the normal amplitude distribution or outside, the ΔWaveECG.From the same PTB-XL database six ECG's were selected, normal, left and right bundle branch block, and three with a myocardial infarction. The normal ECG was obviously within the normal distribution, and all other five showed clear abnormal ECG amplitudes outside the normal distribution in any of the ECG segments (QRS, ST segment and remaining STT segment).The ΔWaveECG can distinguish the abnormal from normal ECG waveform segments, making the ECG easier to classify as normal or abnormal. Conduction disorders and ST changes due to ischemia and abnormal T-waves are effortless to detect, also by non-ECG expert readers, thus improving the early detection of cardiac patients.

Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.

Inherited cardiomyopathies are associated with a broad spectrum of potentially lethal phenotypes characterized by structural and electrical myocardial remodeling. Increased awareness and genetic cascade screening lead to more genotype-positive, yet phenotype-negative individuals to be evaluated and followed up. The predictive value of genetic testing is hampered by incomplete penetrance and high variability in disease onset, progression and severity.Dilated cardiomyopathy usually manifests with symptoms of heart failure and ventricular arrhythmias (VA) develop in advanced disease. In arrhythmogenic cardiomyopathy (ACM), electrical remodeling can precede structural and functional changes and life-threatening VA can be the first disease manifestation. Early signs and symptoms may be subtle and go unnoticed. Physicians are in great need of appropriate screening and risk-stratification strategies. Task Force Criteria (TFC) were established to standardize the clinical diagnosis of ACM but risk-stratification remains challenging. Accurate prediction of disease progression in variation carriers is currently beyond the capabilities of diagnostic tests.We propose three ECG-based techniques; isopotential mapping, inverse ECG and CineECG, to enhance risk-stratification in ACM. With the use of isopotential mapping abnormal spatio-temporal activation and repolarization may be identified. Furthermore, by combining subject specific ≥12‑lead ECG data with cardiothoracic imaging using inverse ECG techniques, the direct link between ECG and cardiac anatomy can be obtained.New ECG techniques may prove more sensitive to detect early de- and repolarization abnormalities in yet asymptomatic variation carriers. Early electrical signs of disease progression may be identified prior to symptoms. Furthermore, individualized risk-stratification may be enhanced.

Longitudinal monitoring of sometimes subtle waveform changes of the 12‑lead electrocardiogram (ECG) is complicated by patient-specific and technical factors, such as the inaccuracy of electrode repositioning. This feasibility study uses a 3D camera to reduce electrode repositioning errors, reduce ECG waveform variability and enable detailed longitudinal ECG monitoring.Per subject, three clinical ECGs were obtained during routine clinical follow-up. Additionally, two ECGs were recorded guided by two 3D cameras, which were used to capture the precordial electrode locations and direct electrode repositioning. ECG waveforms and parameters were quantitatively compared between 3D camera guided ECGs and clinical ECGs. Euclidian distances between original and repositioned precordial electrodes from 3D guided ECGs were measured.Twenty subjects (mean age 65.1 ± 8.2 years, 35% females) were included. The ECG waveform variation between routine ECGs was significantly higher compared to 3D guided ECGs, for both the QRS complex (correlation coefficient = 0.90 vs 0.98, p0.001) and the STT segment (correlation coefficient = 0.88 vs. 0.96, p0.001). QTc interval variation was reduced for 3D camera guided ECGs compared to routine clinical ECGs (5.6 ms vs. 9.6 ms, p = 0.030). The median distance between 3D guided repositioned electrodes was 10.0 [6.4-15.2] mm, and did differ between males and females (p = 0.076).3D guided repositioning of precordial electrodes resulted in, a low repositioning error, higher agreement between waveforms of consecutive ECGs and a reduction of QTc variation. These findings suggest that longitudinal monitoring of disease progression using 12‑lead ECG waveforms is feasible in clinical practice.

We conducted a prospective clinical study (n=14; 29% female) to assess the accuracy of a three-dimensional (3D) photography-based method of torso geometry reconstruction and body surface electrodes localization. The position of 74 body surface electrocardiographic (ECG) electrodes (diameter 5 mm) was defined by two methods: 3D photography, and CT (marker diameter 2 mm) or MRI (marker size 10×20 mm) imaging. Bland-Altman analysis showed good agreement in X (bias −2.5 [95% limits of agreement (LoA) −19.5 to 14.3] mm), Y (bias −0.1 [95% LoA −14.1 to 13.9] mm), and Z coordinates (bias −0.8 [95% LoA −15.6 to 14.2] mm), as defined by the CT/MRI imaging, and 3D photography. The average Hausdorff distance between the two torso geometry reconstructions was 11.17 ± 3.05 mm. Thus, accurate torso geometry reconstruction using 3D photography is feasible. Body surface ECG electrodes coordinates as defined by the CT/MRI imaging, and 3D photography, are in good agreement.