ABSTRACT
Recording of peripheral pulse serves as a very important and essential non-invasive tool used widely by doctors for the diagnosis of various diseases. The morphology of pulse is seen to vary as a function of time in a given individual and also from individual to individual. There are many variations in morphological patterns of peripheral pulse in different disease conditions, which lead to difficulty in accurate diagnosis. The peripheral pulse waveforms are extracted from radial arteries as time series data using a peripheral pulse analyzer which is designed on the principle of impedance plethysmography. It was first introduced by Nyober in the mid-nineteen hundreds and ameliorated further by Kubicek. It involves the recording of the instantaneous blood volume by the measurement of electrical impedance as a function of time. Therefore, the study of peripheral pulse morphology has gained much attention in the past few years among researchers. Physiological variability is one of the recent investigations added during the last two decades for the objective assessment of autonomic function and the assessment of prognosis in severe sicknesses namely myocardial infarction, diabetic neuropathy, etc. In addition to heart rate variability studied worldwide, few researchers have studied blood pressure variability and peripheral blood flow variability. In this computer era, artificial intelligence and machine learning techniques have become more important day-by- day, and different types of algorithms were used for the identification of hidden patterns from plethysmographic observations on the radial pulse such as support vector machine as well as crisp and fuzzy clustering. Eight patterns were classified with a yield of 80%–90% and helped with the diagnosis of disorders such as myocardial infarction, pulmonary tuberculosis, coronary artery disorders, cirrhosis of the liver, and bronchial asthma. This paper briefly describes the use of machine learning techniques for the classification of peripheral pulse morphologies.
ABSTRACT
Background:The objective of this study was to assess the demographical characteristic, laboratory and radiological findings associated with COVID-19 mortality in hospitalizedpatients and also to co-relate neutrophil-to-lymphocyte ratio (NLR)and chest x-ray (CXR)score with severity of the disease.Methods:This is a retrospective study done in Bowring and Lady Curzon hospital between the periodof May 2021 to July 2021. 100 patients who were tested positive for SARS-CoV2 with RT-PCR were taken for the study after fulfilling the inclusion criteria. On day 1 of admission, routine blood investigations including CBC with differential count and chestX ray is taken. From the above said data, NLR and CXR score is calculated and a comparison is made to determine severity and in-hospital mortality between mild, moderate and severe COVID pneumonia patients. This study is being carried out after obtaining institutional ethical committee approval clearance. All analysis were performed using SPSS software version 10.Results: The sample size studied was 100. The mean age of patients was 28.3 in mild, 49.9 in moderate and 62.6 in severe COVID patients. Among these 67% were males and 33% were females. It was noted that, leukocytosis(mean-13245), neutrophilia (mean-83.05%), lymphocytopenia (mean-10.45%) and chest X-ray score (mean-4.98) was seen among severe group with p value being significant.Conclusions: TLC, NLR and CXR score were significantly different between severe and non-severe patients, so assessment of these simple parameters may help identify high risk COVID-19 patients at an early stage in a resource limited setting from the data retrieved from our hospital, NLR and CXR Score showed an acceptable efficiency to separate COVID-19 patients among severe and non-severe patients with a significant p value thereby helping in triaging the patients and need for early ICU needs.