Identification of Patients with Myocardial Infarction. Vectorcardiographic and Electrocardiographic Analysis.

BACKGROUND: The largest morbidity and mortality group worldwide continues to be that suffering Myocardial Infarction (MI). The use of vectorcardiography (VCG) and electrocardiography (ECG) has improved the diagnosis and characterization of this cardiac condition. OBJECTIVES: Herein, we applied a novel ECG-VCG combination technique to identifying 95 patients with MI and to differentiating them from 52 healthy reference subjects. Subsequently, and with a similar method, the location of the infarcted area permitted patient classification. METHODS: We analyzed five depolarization and four repolarization indexes, say: a) volume; b) planar area; c) QRS loop perimeter; d) QRS vector difference; e - g) Area under the QRS complex, ST segment and T-wave in the (X, Y, Z) leads; h) ST-T Vector Magnitude Difference; i) T-wave Vector Magnitude Difference; and j) the spatial angle between the QRS complex and the T-wave. For classification, patients were divided into two groups according to the infarcted area, that is, anterior or inferior sectors (MI-ant and MI-inf, respectively). RESULTS: Our results indicate that several ECG and VCG parameters show significant differences (p-value<0.05) between Healthy and MI subjects, and between MI-ant and MI-inf. Moreover, combining five parameters, it was possible to classify the MI and healthy subjects with a sensitivity = 95.8%, a specificity = 94.2%, and an accuracy = 95.2%, after applying a linear discriminant classifier method. Similarly, combining eight indexes, we could separate out the MI patients in MI-ant vs MI-inf with a sensitivity = 89.8%, 84.8%, respectively, and an accuracy = 89.8%. CONCLUSIONS: The new multivariable MI patient identification and localization technique, based on ECG and VCG combination indexes, offered excellent performance to differentiating populations with MI from healthy subjects. Furthermore, this technique might be applicable to estimating the infarcted area localization. In addition, the proposed method would be an alternative diagnostic technique in the emergency room.

Sleep apnea detection based on spectral analysis of three ECG - derived respiratory signals.

An apnea detection method based on spectral analysis was used to assess the performance of three ECG derived respiratory (EDR) signals. They were obtained on R wave area (EDR1), heart rate variability (EDR2) and R peak amplitude (EDR3) of ECG record in 8 patients with sleep apnea syndrome. The mean, central, peak and first quartile frequencies were computed from the spectrum every 1 min for each EDR. For each frequency parameter a threshold-based decision was carried out on every 1 min segment of the three EDR, classifying it as 'apnea' when its frequency value was below a determined threshold or as 'not apnea' in other cases. Results indicated that EDR1, based on R wave area has better performance in detecting apnea episodes with values of specificity (Sp) and sensitivity (Se) near 90%; EDR2 showed similar Sp but lower Se (78%); whereas EDR3 based on R peak amplitude did not detect appropriately the apneas episodes reaching Sp and Se values near 60%.

Performance evaluation of three methods for respiratory signal estimation from the electrocardiogram.

A comparative study of three methods for estimating respiratory signal through electrocardiogram (ECG) was carried out. The three methods analyzed were based on R wave area, R peak amplitude and heart rate variability (HRV). For each method, cross-correlation coefficient and spectral coherence in a range of frequencies up to 0.5 Hz were computed between the ECG derived respiratory signals (EDR) and the three real respiratory signals: oronasal, and two inductance plethysmographies recordings (chest and abdominal). Results indicate that EDR methods based on R wave area and HRV are better correlated and show a wider spectral coherence with real respiratory signals than the other EDR method based on R peak amplitude.