NIT-R develops new atrial lead system for arrhythmia detection
Bhubaneswar/Rourkela: Researchers from NIT-Rourkela designed a novel lead placement system called the
(ALS) using the electrocardiogram (ECG) machine and its electrodes to detect irregular heart rhythms, or arrhythmias, responsible for a large number of cardiac-related deaths around the world.
Tired of too many ads? go ad free now
Usually, doctors or healthcare workers use an
connected with 12 electrodes to measure the electrical activity of the heart by placing the electrodes on different parts of the body. Lead wires are connected with the electrodes and the ECG machine.
In an ECG, doctors check the 'P-wave', which reflects the electrical activity in the upper chambers of the heart. However, P-waves are often small and easily lost in background noise or obscured by stronger signals from other parts of the heart.
As a result, detecting atrial arrhythmias can sometimes be difficult, particularly in busy clinical settings or with low-cost monitoring equipment, said the researchers.
J Sivaraman, research lead and assistant professor, department of biotechnology and medical engineering, NIT-Rourkela, said lead placement involves positioning these electrodes to capture clear signals from the heart. Placing three leads and positioning the electrodes at the right place on the chest brought wonderful results in improving the accuracy of
, he added.
"The ALS uses a modified arrangement to improve how electrical activity from the atria is recorded. By strengthening these signals, especially the P-wave, the system helps improve the accuracy of arrhythmia detection, both by doctors and by computer-based diagnostic tools," he added.
Other researchers were N Bala Chakravarthy and Kunal Pal, along with research scholars Prasanna Venkatesh and Arya Bhardwaj from NIT-Rourkela, with clinical collaborations and validation provided by Dr R Pradeep Kumar, Institute of Cardiac Care, MIOT International, Chennai.
Tired of too many ads? go ad free now
Findings of this research were published in international journals, including Biomedical Signal Processing & Control, Medical Hypotheses, and Physical & Engineering Sciences in Medicine.
The research team also filed a patent application for the atrial lead system.
This new system helps in easily detecting subtle electrical signals from the upper chambers of the heart, which are often too indistinct to be seen clearly in regular ECGs. These signals play a key role in identifying abnormal heart rhythms that can lead to serious conditions such as
, which can further lead to stroke, said Dr R Pradeep Kumar.
Irregular heart rhythm in the heart's upper chambers, known as atrial arrhythmias, are among the most common, especially in patients who were hospitalised. Identifying these arrhythmias in an early state can help doctors begin timely treatment and prevent complications, said Sivaraman.
The ALS mechanism requires no change to the ECG machine itself. The innovation lies entirely in the way the leads are placed, which means the upgrade can be easily adopted in both public and private healthcare settings without additional cost, said the researchers.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
News18
6 hours ago
- News18
Fake Cardiologist Performs 50+ Surgeries At Faridabad Hospital, Treated Patients Under Stolen ID
Last Updated: The investigation revealed that Dr. Sharma had impersonated another doctor, using Dr. Mohan's registration number. A qualified MBBS doctor, identified as Pankaj Mohan Sharma, posed as a cardiologist and conducted over 50 heart procedures at a heartcare centre in Badshah Khan Civil Hospital over eight months. Despite having a medical degree, Sharma's lack of authorisation for critical cardiac surgeries put numerous lives at risk before his fraudulent practice was exposed. Dr Sharma came under the cops' scanner when Sanjay Gupta, a lawyer and social activist, complained to the NIT Faridabad police station on April 11, quoting sources to allege that he bagged additional degrees based on forged documents. According to the preliminary investigation, the accused had been operating under a stolen identity, misappropriating the National Medical Commission's (NMC) registration number assigned to Dr Pankaj Mohan, a legitimate cardiologist practising in the city. As per the police, Dr. Pankaj Mohan Sharma was hired by Mediterina Hospital, which operates the heart care centre at Badshah Khan Civil Hospital through a public-private partnership, in July last year. The investigation revealed that Dr. Sharma had impersonated another doctor, using Dr. Mohan's registration number 2456, instead of his own MBBS registration number 28482. He did not stop at that. Dr Sharma's prescriptions had a stamp that identified him as a 'cardiologist', complete with a DNB (cardiology) — a postgraduate degree equivalent to an MD. 'The hospital management continued to employ an unqualified and fraudulent doctor who lacks any degree or recognised specialisation in cardiology. Dr Sharma falsely claimed qualifications of MD and DNB (cardiology), which are entirely fraudulent," said lawyer Gupta. Dr. Mohan filed a complaint with the Indian Medical Association in January this year, alleging that Dr. Sharma had illegally used his credentials. Dr. Mohan also served Dr. Sharma a legal notice after discovering the fraudulent activity. Suspicions arose when Dr. Sharma abruptly stopped attending the heart centre in February after being asked to produce his medical degree. Patients began inquiring about him and visited the real Dr. Pankaj Mohan, who clarified that he had never worked at the heart centre, further solidifying the allegations. Additionally, it was discovered that Dr. Sharma had a Facebook profile under the name 'Kshitiz Mohan', where he claimed to be the director of a film production company, casting more doubt on his medical credentials. Dr. M Pratap Kumar, CMD of Mediterina Hospital and head of the heart centre at Badshah Khan, confirmed that Dr. Sharma had been removed from his position. 'He applied through formal HR channels as a general physician. We always had suspicion about his DM (cardiology) degree and acted on it," he said. First Published:
Time of India
a day ago
- Time of India
Curing the incurable: How is AI revolutionising treatment for ALS, Alzheimer's and Dementia?
ALS Detection: Predicting chances of developing Alzheimer's years ahead: Live Events Advancements in Dementia Research with AI: Enhanced patient care with AI-enabled technology: AI ushers in new hope in the fight against neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Alzheimer's, and various forms of dementia. Through the predictive capabilities of machine and deep learning (two subsets of AI), healthcare professionals today are able to predict the onset of these ailments years prior. Enabling early detection uncovers hidden risk factors and allows personalised care, thereby transforming the landscape of diagnosis and treatment. The traditional method of diagnosing ALS stands to be an unnecessarily long procedure, inculcating unwanted spinal surgeries, carpal tunnel releases, and immunotherapies; redundant tests and labs; merely to exclude every other possible condition that could align with the symptoms the patient shows. As a result, this procedure could take up to a year or even more. Before this procedure procures information that could successfully diagnose the patient, the damage stands to be made, making it too late for any treatment to garner AI and its ability to analyse vast amounts of data enable identifying early signs of the disease. For example, the uses AI to observe speech patterns, motor abilities, and the individual's manner of walking. The AI notices subtle changes portrayed by the individual that the human eye tends to have trouble noticing until made explicitly evident. Integrating AI in the diagnosis of ALS makes it faster, giving patients more time to treat themselves with an increased amount of hope than with traditional also enables diagnosis of patients who do not have any mentions of ALS in their medical records but could have undiagnosed ALS by recognising patterns in their medical histories. This reduces time and allows faster intervention, leading to better patient outcomes. Scientists at UCSF have developed a way to predict Alzheimer's disease up to seven years before the symptoms start to appear via having machine learning analyse patient records. The machine learning analyses the patient's electronic health records and also considers factors such as high cholesterol, osteoporosis (especially in women), depression, and vitamin D deficiency, thereby fostering a success rate of nearly 72%.This early prediction model allows patients to try various treatment plans, provides them the liberty to hope for improvement, and also lets them make lifestyle modifications that could procure delays or mitigations in the progression of Alzheimer' is an amalgamation of various diseases caused by various underlying disorders like Alzheimer's, vascular dementia frontotemporal dementia , and more. Each kind of dementia varies with the different ancestral backgrounds, environmental exposures, and genetics. With this kind of complexity, it is inevitable that there will occur a large data set. Thereby, making the inculcation of AI into dementia-related studies essential, as emphasised by the NIH's Center for Alzheimer's and Related Dementias (CARD). AI's capacity to analyse complex and large datasets uncovers subtle patterns in data, aiding in early detection and diagnosis of various dementia types and understanding their AI is exceptional in identifying previously unknown genetic risk factors, enhancing healthcare professionals' understanding of the ailment's biology, and paving the way for personalised from being entangled in vital research-related roles and predictive diagnosis-related roles in healthcare, AI is also equally entangled in caregiving. AI-powered robots are being explored by researchers to support dementia patients in their daily lives, promoting independence and reducing familial burden. These robots are being studied/built with the aim to respect the autonomy and dignity of patients while providing practical help with tasks such as medication reminders and emergency alerts. Innovations such as these are bound to be an improving agent for the patients' quality of life and offer reassurance to the existence within this field shall potentially revolutionise the approaches taken towards the ailments deemed incurable, offering hope for earlier diagnoses, personalised treatments, and improved patient outcomes. As studies advance and technology evolves, AI is equipped to become an invulnerable warrior in this ongoing battle against ALS, Alzheimer's, and dementia of other forms ( source ).
Economic Times
a day ago
- Economic Times
Curing the incurable: How is AI revolutionising treatment for ALS, Alzheimer's and Dementia?
AI ushers in new hope in the fight against neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Alzheimer's, and various forms of dementia. Through the predictive capabilities of machine and deep learning (two subsets of AI), healthcare professionals today are able to predict the onset of these ailments years prior. Enabling early detection uncovers hidden risk factors and allows personalised care, thereby transforming the landscape of diagnosis and treatment. The traditional method of diagnosing ALS stands to be an unnecessarily long procedure, inculcating unwanted spinal surgeries, carpal tunnel releases, and immunotherapies; redundant tests and labs; merely to exclude every other possible condition that could align with the symptoms the patient shows. As a result, this procedure could take up to a year or even more. Before this procedure procures information that could successfully diagnose the patient, the damage stands to be made, making it too late for any treatment to garner improvements. However, AI and its ability to analyse vast amounts of data enable identifying early signs of the disease. For example, the uses AI to observe speech patterns, motor abilities, and the individual's manner of walking. The AI notices subtle changes portrayed by the individual that the human eye tends to have trouble noticing until made explicitly evident. Integrating AI in the diagnosis of ALS makes it faster, giving patients more time to treat themselves with an increased amount of hope than with traditional also enables diagnosis of patients who do not have any mentions of ALS in their medical records but could have undiagnosed ALS by recognising patterns in their medical histories. This reduces time and allows faster intervention, leading to better patient at UCSF have developed a way to predict Alzheimer's disease up to seven years before the symptoms start to appear via having machine learning analyse patient records. The machine learning analyses the patient's electronic health records and also considers factors such as high cholesterol, osteoporosis (especially in women), depression, and vitamin D deficiency, thereby fostering a success rate of nearly 72%. This early prediction model allows patients to try various treatment plans, provides them the liberty to hope for improvement, and also lets them make lifestyle modifications that could procure delays or mitigations in the progression of Alzheimer's. Dementia is an amalgamation of various diseases caused by various underlying disorders like Alzheimer's, vascular dementia, Lewy body dementia, frontotemporal dementia, and more. Each kind of dementia varies with the different ancestral backgrounds, environmental exposures, and genetics. With this kind of complexity, it is inevitable that there will occur a large data set. Thereby, making the inculcation of AI into dementia-related studies essential, as emphasised by the NIH's Center for Alzheimer's and Related Dementias (CARD). AI's capacity to analyse complex and large datasets uncovers subtle patterns in data, aiding in early detection and diagnosis of various dementia types and understanding their interrelations. Moreover, AI is exceptional in identifying previously unknown genetic risk factors, enhancing healthcare professionals' understanding of the ailment's biology, and paving the way for personalised therapies. Apart from being entangled in vital research-related roles and predictive diagnosis-related roles in healthcare, AI is also equally entangled in caregiving. AI-powered robots are being explored by researchers to support dementia patients in their daily lives, promoting independence and reducing familial burden. These robots are being studied/built with the aim to respect the autonomy and dignity of patients while providing practical help with tasks such as medication reminders and emergency alerts. Innovations such as these are bound to be an improving agent for the patients' quality of life and offer reassurance to the caregivers. AI's existence within this field shall potentially revolutionise the approaches taken towards the ailments deemed incurable, offering hope for earlier diagnoses, personalised treatments, and improved patient outcomes. As studies advance and technology evolves, AI is equipped to become an invulnerable warrior in this ongoing battle against ALS, Alzheimer's, and dementia of other forms (source). Disclaimer Statement: This content is authored by a 3rd party. The views expressed here are that of the respective authors/ entities and do not represent the views of Economic Times (ET). ET does not guarantee, vouch for or endorse any of its contents nor is responsible for them in any manner whatsoever. Please take all steps necessary to ascertain that any information and content provided is correct, updated, and verified. ET hereby disclaims any and all warranties, express or implied, relating to the report and any content therein.
