ABSTRACT
Diseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is common for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected as early as possible. This article presents an early prototype of a device that can be compared to a digital stethoscope that performs auto-breath analysis. So apart from recording the respiratory cycles, the device also analyzes them. In addition, it also has the functionality of notifying the user (e.g. via a smartphone) about the need to go to the doctor for a more detailed examination. The audio recording of breath cycles is transformed to a two-dimensional matrix using mel-frequency cepstrum coefficients (MFCC). Such a matrix is analyzed by an artificial neural network. As a result of the research, it was found that the best of the obtained solutions of the presented neural network achieved the desired accuracy and precision at the level of 84%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Diseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is common for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected as early as possible. This article presents an early prototype of a device that can be compared to a digital stethoscope that performs auto-breath analysis. So apart from recording the respiratory cycles, the device also analyzes them. In addition, it also has the functionality of notifying the user (e.g. via a smartphone) about the need to go to the doctor for a more detailed examination. The audio recording of breath cycles is transformed to a two-dimensional matrix using mel-frequency cepstrum coefficients (MFCC). Such a matrix is analyzed by an artificial neural network. As a result of the research, it was found that the best of the obtained solutions of the presented neural network achieved the desired accuracy and precision at the level of 84%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Purpose: To identify factors associated with an increased or decreased risk of SUDEP. Method: The EpiNet study group is undertaking a prospective case-control study, aiming to recruit 200 participants from approximately 40 international centres over four years. Patients with epilepsy from a pre-defined cohort who die of definite or probable SUDEP will be included. Cases must be alive when the cohort is defined. For each case, three true controls and one proxy control will be recruited from the same cohort. A structured telephone interview with the next-of-kin of SUDEP cases will be conducted. Controls will be asked about their epilepsy and lifestyle. Proxy controls will be asked about the control patient they know. Information regarding seizure type and medication, sleeping arrangements, nocturnal supervision, use of seizure-detection devices, socio-economic factors and other health issues will be entered into the EpiNet database. Pathologists' and coroners' data regarding circumstances and cause of death will also be recorded if available. The data will be analysed to identify risk factors for SUDEP. Odds ratios will be calculated using the Mantel-Haenszel method and logistic regression to control for covariates. 200 cases and 800 controls will detect an odds ratio of 1.7 over a control exposure range of 22-65%, with 80% power and 95% confidence level (2-sided). Result: The study is now underway in 8 countries through Asia-Oceania, Europe and North America. COVID-19 has adversely affected case enrolment, and new centres are being sought. Conclusions: SUDEP is second only to stroke as the leading neurological cause of years of potential life lost. The causes remain uncertain. A large prospective case-control study is the best way to determine the extent of the association between specific variables and SUDEP, in particular, those that could be modified to prevent this tragedy. Anyone interested in participating is welcome to contact: epinetadmin@adhb.govt.nz.
ABSTRACT
Background and aims: One fifth of patients with COVID- 19 may develop neurological symptoms. High rates of electroencephalographic (EEG) abnormalities were reported patients with SARS-CoV-2 infection and altered mental state. The aim of this study was to review the EEG findings in Polish patients with impaired consciousness and COVID- 19 infection. Methods: We retrospectively reviewed medical records and EEG studies of patients with COVID-19 infections and impairment of consciousness hospitalized in 2020 in University Hospital in Kraków. Results: We analyzed 23 EEG performed in 18 patients, 61% (11) were females (median age 62,3 years). SARSCoV- 2 infection was the main cause of hospitalization in only 11,1% subjects. The remaining patients were hospitalized due to neurological disorders. Clinically significant MR or CT scan were found in 10 patients (55,56%) and CSF abnormalities in 7(38,8%). 14 (77,78%) patients took psychoactive drugs at time of EEG: 9(50%) took antiseizure medication and 5(27,78%) psychiatric drugs. EEG was normal in three (13%) patients. The most frequent EEG finding (9;39,13%), was generalized slowing in theta/delta range. Focal slowing was found in two patients. Epileptiform discharges were present in three (16,67%) patients and five (21,7%) EEG. Seizures were recorded in three EEG (13,04%), focal status epilepticus in 2 EEG (8,7%). Among patients with epileptiform discharges 2(75%) had a history of epilepsy/seizures, 1(25%) had abnormalities in neuroimaging (changes specific to PRES) and 1(25%) had elevated level of cytosis (7 cells) in CSF. Conclusion: Abnormal background activity is common in COVID-19 and altered mental status. The majority of patients with epileptiform discharges have a history of epilepsy/seizures.