Vectorial analysis of the electrocardiogram from conventional limb leads in healthy adult Nepalese females

Introduction: ECG interpretation plays a vital role in the initial evaluation of patients presenting with cardiac complaints. Assessment of degree of cardiac vector is one of the important parameters to be studied in ECG. Methods: A prospective cross-sectional study was carried out among 84 female subjects, aged 18- 40 years of age, including students and staff of Gandaki Medical College Teaching hospital and Research Center, Pokhara, Nepal over a period of one month from 1st Oct to 1st Nov 2021. A Standard ECG machine was used and the ECG was recorded using the conventional limb leads. The individual mean electrical axis of the heart was plotted using the net voltage of QRS complex of Lead-I and Lead-III. The possible correlation between cardiac vector and physical measurements like height weight BSA and BMI were analyzed. The data were analyzed in SPSS version 27. Results: The normal mean electrical axis of the healthy female subjects was observed as 61.7±23.51°. There was a significant positive correlation of cardiac vector with height (p< 0.05), whereas a negative correlation was observed with weight and BMI (p< 0.01). However, there was no significant correlation with BSA. In our study, we observed the maximum left axis cardiac vector as -2° and right axis as 98° among 84 female subjects. Conclusion: Documentation of cardiac vector was made using standard bipolar limb leads in normal healthy female subjects. BMI is involved in the deviation of cardiac vector with a negative correlation. This observation could make it quite attractive for use in clinical practice.

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Pediatric Transthoracic Cardiac Vector Flow Imaging – A Preliminary Pictorial Study

Purpose Conventional pediatric echocardiography is crucial for diagnosing congenital heart disease (CHD), but the technique is impaired by angle dependency. Vector flow imaging (VFI) is an angle-independent noninvasive ultrasound alternative for blood flow assessment and can assess complex flow patterns not visible on conventional Doppler ultrasound. Materials and Methods 12 healthy newborns and 3 infants with CHD were examined with transthoracic cardiac VFI using a conventional ultrasound scanner and a linear array. Results VFI examinations revealed common cardiac flow patterns among the healthy newborns, and flow changes among the infants with CHD not previously reported with conventional echocardiography. Conclusion For assessment of cardiac flow in the normal and diseased pediatric heart, VFI may provide additional information compared to conventional echocardiography and become a useful diagnostic tool.