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Abstract-Diagnosis of congenital cardiac defects is challenging, with some being diagnosed during pregnancy while others are diagnosed after birth or later on during childhood. Prompt diagnosis allows early intervention and best prognosis. Contemporary diagnosis relies upon the history, clinical examination, pulse oximetery, chest X-ray, electrocardiogram (ECG), echocardiography (ECHO), computed tomography (CT) and cardiac catheterization. These diagnostic modalities reliable upon recording electrical activity or sound waves or upon radiation. Yet, congenital heart diseases are still liable to misdiagnosis because of level of operator expertise and other multiple factors. In an attempt to minimize effect of operator expertise this paper built a classification model for heart murmur recognition using Hidden Markov Model (HMM). This paper used Mel Frequency Cepestral coefficient (MFCC) as a feature and 13 MFCC coefficients. The machine learning model built by studying 1069 different heart sounds covering normal heart sounds, ventricular septal defect (VSD), mitral regurgitation (MR), aortic stenosis (AS), aortic regurgitation (AR), patent ductus arteriosus (PDA), pulmonary regurgitation (PR), and pulmonary stenosis (PS). MFCC feature used to extract feature matrix for each type of heart sounds after separation according to amplitude threshold. The frequency of normal heart sound (range= 1Hz to 139Hz) was specific without overlap with any of the studied defects (ranged= 156-556Hz). The frequency ranges for each of these defects was typical without overlap according to examined heart area (aortic, pulmonary, tricuspid and mitral area). The overall correct classification rate (CCR) using this model was 96% and sensitivity 98%. This model has great potential for prompt screening and specific defect detection. Effect of cardiac contractility, cardiomegaly or cardiac electrical activity on this novel detection system needs to be verified in future works.

Background: Impaired activity of respiratory muscles and poor lung mechanisms predispose to sleep disordered breathing in neuromuscular disorders. Although it may lead to major morbidity, its relation to respiratory function is poorly defined. Objectives:To evaluate respiratory muscle function and sleep disorders in children with myopathy and their relationship to daytime and nocturnal symptoms and oxygen saturation. Method:A cross-sectional study was carried out on 30 children, 20 males and 10 females, diagnosed with myopathy at Abou El Reesh Paediatric Hospital, Cairo University. Arterial blood gases, creatine kinase (CK), aspartate transaminase (AST) and alanine transaminase (ALT) levels were measured. All subjects underwent respiratory function tests using spirometry, overnight polysomnography and diaphragmatic ultrasound. Results:Patients were assigned into 2 groups based on respiratory function tests assessed by spirometry. Group A included 14 patients with normal respiratory function tests and Group B included 16 patients with abnormal respiratory function tests. A significant difference was noted as regards symptoms suggestive of poor sleep quality, including somnolence, waking unrested and frequent awakening (p=0.005). Apnoea hypopnoea index (AHI) was significantly higher in group B patients (p=0.04). AHI was abnormal in 43% of patients in group A and 69% of patients in group B. Obstructive apnoeic and hypopnoeic events were detected in all patients with abnormal AHI. No significant difference was noted regarding sleep staging, sleep efficiency or total sleep test (TST).