QT and P-wave dispersion during the manic phase of bipolar disorder

A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals

Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates the diagnosis. It's critical to distinguish between bipolar disorder, depression, and schizophrenia since their symptoms and treatments differ. Because of brain-heart autonomic connections, electrocardiography (ECG) signals can be changed in behavioral disorders. In this research, we have automatically classified bipolar, depression, and schizophrenia from ECG signals. In this work, a new hand-crafted feature engineering model has been proposed to detect psychiatric disorders automatically. The main objective of this model is to accurately detect psychiatric disorders using ECG beats with linear time complexity. Therefore, we collected a new ECG signal dataset containing 3,570 ECG beats with four categories. The used categories are bipolar, depression, schizophrenia, and control. Furthermore, a new ternary pattern-based signal classification model has been proposed to classify these four categories. Our proposal contains four essential phases, and these phases are (i) multileveled feature extraction using multilevel discrete wavelet transform and ternary pattern, (ii) the best features selection applying iterative Chi2 selector, (iii) classification with artificial neural network (ANN) to calculate lead wise results and (iv) calculation the voted/general classification accuracy using iterative majority voting (IMV) algorithm. tenfold cross-validation is one of the most used validation techniques in the literature, and this validation model gives robust classification results. Using ANN with tenfold cross-validation, lead-by-lead and voted results have been calculated. The lead-by-lead accuracy range of the proposed model using the ANN classifier is from 73.67 to 89.19%. By deploying the IMV method, the general classification performance of our ternary pattern-based ECG classification model is increased from 89.19 to 96.25%. The findings and the calculated classification accuracies (single lead and voted) clearly demonstrated the success of the proposed ternary pattern-based advanced signal processing model. By using this model, a new wearable device can be proposed.

Increased Dispersion of Ventricular Repolarization as an Arrhythmic Risk Marker in Drug-free Patients With Major Depressive Disorder: A Preliminary Comparative Study

OBJECTIVE

Drug-free patients with major depressive disorder (MDD) are understudied in terms of increased risk for arrhythmias. In this study, we compared changes in corrected QT interval (QTc), QTc dispersion (QTcd), Tpeak-Tend (Tp-e), Tp-e/QT ratio, corrected JT interval (JTc), and JTc dispersion (JTcd), which are considered to be among the risk factors for the emergence of ventricular arrhythmias in patients with MDD.

METHODS

The study involved 50 patients with MDD who had been free of psychotropic medications for at least 1 month and 52 age-matched and sex-matched healthy controls. Illness-related characteristics, including duration of illness and Beck Depression Inventory scores, were recorded. Electrocardiography recordings made under a standardized procedure were performed for all participants, and arrhythmia risk markers were calculated from the electrocardiograms.

RESULTS

The patient group had significantly higher QTcd, JTc, and JTcd values compared with the controls. Among electrocardiogram markers, only Tp-e/QTc was significantly and inversely correlated with the duration of illness, while none of the markers was associated with Beck Depression Inventory scores.

CONCLUSIONS

Alterations in electrocardiogram-derived markers of ventricular arrhythmia, which can be obtained easily and inexpensively, can be evaluated for the prediction and prevention of severe cardiac conditions in patients with MDD and considered in selecting the safest antidepressant options available.

Electrocardiographic Evaluation of the Ventricular Arrhythmia Risk in Patients Diagnosed With Schizophrenia

OBJECTIVE

The purpose of this study was to examine electrocardiographic ventricular arrhythmia predictors in patients with schizophrenia by comparing with healthy controls.

METHODS

The study included 100 patients with schizophrenia and 100 healthy controls. Electrocardiography (ECG) was performed on all participants in resting position. T-wave peak to end (Tp-e), QT ranges, P-wave dispersion (Pd), and R-R range were measured. Then, the Schizophrenia Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS) were applied to the schizophrenia group.

RESULTS

The PANSS positive symptom subscale was calculated as 10.41 (SD = 2.27), the negative symptom subscale was calculated as 14.44 (SD = 5.42), and the overall functionality level was calculated as 27.04 (SD = 5.43). The mean CDSS score was determined to be 3.74 (SD = 2.15). No differences were detected in the heart rate measurements of the patient and control groups in ECG results (P = .427). The minimum QT interval and minimum Tp-e wave times were found to be low in the patient group (P < .001 for both intervals). Corrected QTc dispersion, Pd, Tp-e dispersion, and QT dispersion were found to be higher in the patient group than in healthy controls (P < .001 for all intervals).

DISCUSSION

Based on our results, it is possible to speculate that patients with schizophrenia are at a risk of developing cardiac arrhythmia and cardiac dysfunction if they do not receive treatment. For this reason, clinicians should pay attention to cardiac transmission problems when organizing the treatment of patients. Further studies should be conducted to determine cardiac problems in patients with schizophrenia.

ECG changes in patients with opioid use disorder; P-QT wave dispersion: a retrospective study

OBJECTIVE

We aimed to examine the electrocardiographic arrhythmia risk predictors in patients with opioid use disorder by P wave dispersion, QT dispersion, and by comparing Tp-e/QTc ratio with healthy controls.

METHODS

One hundred seventeen patients who were diagnosed with opioid use disorder according to DSM-5 criteria and who were hospitalized in 25 Aralık Gaziantep State Hospital AMATEM (Alcohol and substance addiction treatment center) service and a healthy control group consisting of 168 subjects were included in the study. Electrocardiography (ECG) scans of all participants were performed in the supine position and at rest. P wave dispersion, QT dispersion, and Tp-e/QTc ratio were calculated.

RESULTS

Of all the participants, 12 were women (4.21%), 273 (95.78%) were men. The mean age of all participants was 30.42 ± 9.36 years. No difference was found between the gender, mean age, marital status, educational level, and smoking status of the participants (p > 0.05). The entire patient group was using heroin. Heart rate of the patient group was calculated as 68.14 ± 13.26 beats per minute, being lower than healthy controls (p < 0.05). Although QT dispersion value was lower than healthy controls (p < 0.05); P wave dispersion did not differ between groups (p > 0.05).

CONCLUSION

It is thought that the patients with opioid use disorder are at risk for cardiac arrhythmia based on our findings. Therefore, physicians should be careful about cardiac rhythm and conduction problems while organizing any treatment of these patients.