ABSTRACT
Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.
Subject(s)
COVID-19 , Cardiovascular Diseases , Heart Sounds , COVID-19/diagnosis , COVID-19 Testing , Cardiovascular Diseases/diagnosis , Humans , Pandemics , Phonocardiography/methods , Signal Processing, Computer-AssistedABSTRACT
When cardiovascular issues arise in a cardiac patient, it is essential to diagnose them as soon as possible for monitoring and treatment would be less difficult than in the old. Paediatric cardiologists have a difficult time keeping track of their patients’ cardiovascular condition. To accomplish this, a phonocardiogram (PCG) device was created in combination with a MATLAB software based on artificial intelligence (AI) for automatic diagnosis of heart state classification as normal or pathological. Due to the safety concerns associated with COVID-19, testing on school-aged children is currently being explored. Using PCG analyses and machine learning methods, the goal of this work is to detect a cardiac condition, whilst operating on a limited amount of computing resources. This makes it possible for anybody, including non-medical professionals, to diagnose cardiac issues. To put it simply, the current system consists of a distinct portable electronic stethoscope, headphones linked to the stethoscope, a sound-processing computer, and specifically developed software for capturing and analysing heart sounds. However, this is more difficult and time-consuming, and the accuracy is lowered as a result. According to statistical studies, even expert cardiologists only achieve an accuracy of approximately 80%. Nevertheless, primary care doctors and medical students usually attain a level of accuracy of between 20 and 40%. Due to the nonstationary nature of heart sounds and PCG's superior ability to model and analyse even in the face of noise, PCG sounds provide valuable information regarding heart diseases. Spectral characteristics PCG is used to characterise heart sounds in order to diagnose cardiac conditions. We categorise normal and abnormal sounds using cepstral coefficients, or PCG waves, for fast and effective identification, prompted by cepstral features’ effectiveness in speech signal classification. On the basis of their statistical properties, we suggest a new feature set for cepstral coefficients. The PhysioNet PCG training dataset is used in the experiments. This section compares KNN with SVM classifiers, indicating that KNN is more accurate. Furthermore, the results indicate that statistical features derived from PCG Mel-frequency cepstral coefficients outperform both frequently used wavelet-based features and conventional cepstral coefficients, including MFCCs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
BACKGROUND: Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients. OBJECTIVE: The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope. METHODS: We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope. RESULTS: We were able to conveniently operate Auscul Pi and precisely record the patients' auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001). CONCLUSIONS: Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19. TRIAL REGISTRATION: ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971.
ABSTRACT
Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.