ECG Signal Analysis for Myocardial Infarction DetectionUzair Akbar
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
In many situations, the Electrocardiogram (ECG) is
recorded during ambulatory or strenuous conditions such that the
signal is corrupted by different types of noise, sometimes
originating from another physiological process of the body. Hence,
noise removal is an important aspect of signal processing. Here five
different filters i.e. median, Low Pass Butter worth, FIR, Weighted
Moving Average and Stationary Wavelet Transform (SWT) with
their filtering effect on noisy ECG are presented. Comparative
analyses among these filtering techniques are described and
statically results are evaluated.
ECG Signal Analysis for Myocardial Infarction DetectionUzair Akbar
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.
In many situations, the Electrocardiogram (ECG) is
recorded during ambulatory or strenuous conditions such that the
signal is corrupted by different types of noise, sometimes
originating from another physiological process of the body. Hence,
noise removal is an important aspect of signal processing. Here five
different filters i.e. median, Low Pass Butter worth, FIR, Weighted
Moving Average and Stationary Wavelet Transform (SWT) with
their filtering effect on noisy ECG are presented. Comparative
analyses among these filtering techniques are described and
statically results are evaluated.
A Novel Extended Adaptive Thresholding for Industrial Alarm SystemsKoorosh Aslansefat
Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system’s performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.
A Computationally Efficient QRS Detection Algorithm for Wearable ECG Sensors ecgpapers
In this paper we present a novel Dual-Slope QRS
detection algorithm with low computational complexity,
suitable for wearable ECG devices. The Dual-Slope algorithm
calculates the slopes on both sides of a peak in the ECG
signal; And based on these slopes, three criterions are
developed for simultaneously checking 1)Steepness 2)Shape
and 3)Height of the signal, to locate the QRS complex. The
algorithm, evaluated against MIT/BIH Arrhythmia Database,
achieves a very high detection rate of 99.45%, a sensitivity of
99.82% and a positive prediction of 99.63%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
1. Jitter recordings with
concentric needle electrode
Erik Stålberg
Most of the voluntary signals are recorded by Stålberg
All stimulation signals are recorded by Kouyoumdjian (shown with permission)
2. Filter: SFEMG 500Hz-10KHz
CNE for jitter analysis 1KHz-10KHz
Sweep speed: 0,5 msec/div (always less than 2 msec/div)
Gain: not crucial. Make signals to cover about 2 divisions
For quality control during and after recording, superimpose 5-
15 sweeps.
3. Definitions of acceptable CNE signals
for jitter analysis
Monotonously rising positive negative signals without inflections, notches or shoulders.
Parallel rising segments upon superimposition (5-15)
(Slight disturbance, e.g. influence from remote fibers, can be accepted if it occurs close to
the positive peak, lower than 50% of rise time)
Negative peaks should be separated by more than 150 usec
Only slight amplitude variation in the signal, else summation from same MU or background
activity
VOLUNTARY ACTIVATION:
Above + Regular firing rate
ELECTRICAL STIMULATION:
Above + supraliminal stimulation intensity
8. Looks like impulse blocking at traces with arrows. This is not so,
but false trigger. The large signals on these traces have another
shape (broader, higher) than the neighbours
Normal
Voluntary
9. The first spike obviously composed of 2 fibers. It it still accepted, since the disturbacance
is near the positive peak and most likely gives only minor influence to the jitter value.
Normal
Voluntary
14. El stim. Signals with arrow are not accepted; non parallel rising phase or
disturbance in the negative peak, which is used for measurements
Stimulation
25. Jitter measurements of separated signals :
points crossing amplitude level or peaks
Trig level
Stålberg
start
stop
for signals within
the window
trig
time window
start
time window
stop
Methods for jitter analysis. In the original method, level trigger, time measurements was made between the
moments when the signal passed a predefined amplitude level (time windows, start and stop).
In a more recent method, peak trigger, measurements are made between mathematically calculated negative
peak of each spike (full arrows).
In this example, the spikes are well separated, and the two methods give the same numerical result
26. Jitter measurements of riding signals :
points crossing amplitude level or peaks
Stålberg
Trig level
start
In this fig it is seen that errors in time measurments occur if the signals are “riding” on each other using time
amplitude level method. Therefore peak trigger method is recommended, now available in some EMG equipment.
27. Errors in jitter measurements
Voluntary activation
• Triggering spike
• IPI measures; level or peak
• VRF
• Long IPI (should be < 4 msec)
Electrical stimulation
• Subliminal stimulation
• Irregular freq (stim, F, doublet)
• Axon reflex
Pitfalls in jitter measruements, Stålberg, Sanders, Kouyoumdjian
Clin Neurophysiol, 2017
28. 1_2 1_3 1_4 jSUM
48.8 46.1 94.2 189.1
2_1 2_3 2_4
48.8 34.6 92.2 175.6
3_1 3_2 3_4
46.1 34.6 91.3 172.0
4_1 4_2 4_3
94.2 92.2 91.3 277.7
Different jitter values depending on choice of trigger. A sum of jitter values (jsum) indicates the optimal choice, in this case
trigger on spike 3. Spike 4 not useful because of slow rise-time
ALS
Arrow indicate
chosen trig
29. *
Signals on first 3 traces likely from the same axon. Spikes with arrow and * from 2 other axons
30. 20 µs 45 µs
Right Frontalis
0.5mV/D 0.3 ms/D
13 µs8 µs
A B
20 µs 45 µs
El stim
accepted accepted accepted not accepted
88 µs 13 µs
31. MCD = 150 µs
Supramaximal for some spikes.
Many individual spikes show
independent blocking (arrows)
MCD = 262 µs
Submaximal
Only 1 spike and blocking
This shows how difficult
the interpretation may be
32. 0-2 Hz
0-5 Hz
0-10 Hz
0-15 Hz
0-20 Hz
0-1 Hz
El stim.
Constant stim
intensity
except for
induced
Pauses.
Note the effect of
VRF,particularly at
higher stim
frequencies
Stålberg
33. Axon reflex with distinctly separated latencies
Courtesy Kouyoumdjian
34. Individual blocking in reinnervation;
spikes probably representing different axons
Blocking components behave independently
Courtesy Kouyoumdjian
35. VRF effect seen
after the blocking
of on impulse
Gives a jitter values of
54 usec
Poems disease Courtesy Kouyoumdjian
