Ambulatory Electroencephalography

2009 ◽  
pp. 187-192 ◽  
Author(s):  
Jeffrey R. Buchhalter
Keyword(s):  
Antiepileptic Drugs ◽  
Sampling Rate ◽  
Seizure Detection ◽  
Outpatient Setting ◽  
Digital Systems ◽  
Detection Algorithms ◽  
Continuous Eeg ◽  
Cost Efficient ◽  
Rate Frequency

Ambulatory EEG (AEEG) can provide a cost-efficient means of recording prolonged, continuous EEG in the outpatient setting. The quality of digital systems is comparable to in-lab recording—number of available channels, sampling rate, frequency bandwidth, sharp transient/seizure detection algorithms, and the ability to reformat studies. AEEG is an effective technology to distinguish epileptic from non-epileptic events in addition to classification, quantification, and localization of seizures. Limitations of the technique are the inability to immediately secure loose electrodes and to decrease antiepileptic drugs without direct observation by EEG technologists and nursing personnel.

scholarly journals Effects of sampling rate on automated fatigue recognition in surface EMG signals

2015 ◽  
Vol 1 (1) ◽  
pp. 80-84 ◽  
Author(s):  
Lorenz Kahl ◽  
Marcus Eger ◽  
Ulrich G. Hofmann
Keyword(s):  
Sampling Rate ◽  
Approximate Entropy ◽  
Surface Emg ◽  
Median Frequency ◽  
Spectral Moment ◽  
Detection Algorithms ◽  
Moment Ratio ◽  
Wide Range ◽  
Fatigue Detection

AbstractThis study investigated the effects different sampling rates may produce on the quality of muscle fatigue detection algorithms. sEMG signals were obtained from isometric contractions of the arm. Subsampled signals resulting in technically relevant sampling rates were computationally deduced from the original recordings. The spectral based fatigue recognition methods mean and median frequency as well as spectral moment ratio were included in this investigation, as well as the sample and the fuzzy approximate entropy. The resulting fatigue indices were evaluated with respect to noise and separability of different load levels. We concluded that the spectral moment ratio provides the best results in fatigue detection over a wide range of sampling rates.

Quality of laboratory tests as a basic component of treatment effectiveness

2020 ◽  
pp. 38-47
Author(s):  
Tamara Silkina ◽  
Olga Petrova
Keyword(s):  
General Practitioner ◽  
Russian Federation ◽  
Laboratory Tests ◽  
Treatment Effectiveness ◽  
Outpatient Setting ◽  
Basic Component ◽  
The Russian Federation

The article presents analysis of modern laboratory technologies and requirements for the quality of laboratory tests in the Russian Federation. Basic rules that improve the quality of laboratory tests at the preanalytical, analytical and postanalytical stage are studied on the example of tests in the Laboratory Hemotest. The optimal list of laboratory tests used in the practice of a general practitioner and organizational options for performing laboratory tests, the features of the process that affect the speed of obtaining a result by a doctor in an outpatient setting and in hospital, are presented.

scholarly journals There and back again: analyzing the effect of outpatient readmission on the quality of life of patients attending a rheumatology clinic

2021 ◽  
Vol 13 ◽  
pp. 1759720X2110348
Author(s):  
Alfredo Madrid-García ◽  
Isabel Montuenga-Fernández ◽  
Judit Font-Urgelles ◽  
Leticia León-Mateos ◽  
Esperanza Pato ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Outpatient Clinic ◽  
Outpatient Setting ◽  
P Value ◽  
Marginal Effect ◽  
Rheumatology Clinic ◽  
Related Quality ◽  
Health Related ◽  
San Carlos

Aims: The aim of this study was to assess the effect of “outpatient readmissions” on the health-related quality of life (HR-QoL) of outpatients from a rheumatology clinic, meaning the effect of the patient’s return to the outpatient clinic after having received care and been discharged. Methods: We conducted an observational longitudinal retrospective study, with patients selected from the Hospital Clínico San Carlos Musculoskeletal cohort, based on having received at least one discharge from the outpatient clinic and having returned (readmission) at least once after the discharge. The main outcomes were the patients’ baseline HR-QoL (measured on the first visit of each episode) and the ΔHR-QoL (difference between the HR-QoL in the last and the first visit of each episode). Successive episodes of admission and readmission were chronologically ordered, paired and analyzed using nested linear mixed models, nested by patients and by admission–readmission tandem. We carried out bivariable and multivariable analyses to assess the effect of demographic, clinical, treatment and comorbidity-related variables in both main outcomes. Results: For the first main outcome, 5887 patients (13,772 episodes) were analyzed. Based on the multivariable level, readmission showed no significant marginal effect on the baseline HR-QoL ( p-value = 0.17). Conversely, when analyzing the ΔHR-QoL, we did observe a negative and significant marginal effect ( p-value = 0.028), meaning that readmission was associated with a lower gain in the HR-QoL during the follow-up, compared with the previous episode. Conclusion: In the outpatient setting, readmission exerts a deleterious effect in patients undergoing this process. Identification of outpatients more likely to be readmitted could increase the value of the care provided.

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

A ROLE OF AYURVEDA IN THE MANAGEMENT OF APASMARA (EPILEPSY): A CASE STUDY

2021 ◽  
Vol 12 (4) ◽  
pp. 24-29
Author(s):  
Shruti . ◽  
C V Rajashekhar ◽  
Manjunatha Adiga
Keyword(s):  
Antiepileptic Drugs ◽  
Palliative Treatment ◽  
Cognitive Level ◽  
Frequent Seizure ◽  
Seizure Attack ◽  
Seizure Attacks

Apasmara (epilepsy) is defined as the apagama (deterioration) of smriti (memory) associated with bibhasta cheshta (seizures) due to derangement of dhi and satwa, mainly related to vata and rajo dosha vitiation, which effects both Sharira (body) and Mana (mind). The present antiepileptic drugs control the seizure attack, but long-term use generates adverse effect at cognitive level and leads to behavioral disorders, hence there is need of safe and effective treatment which not only controls seizure attack but helps to cure the disease. A 44-year-old man approached Kayachikitsa OPD with the complaints of frequent seizure attacks, since from at the age of one and half year with regular oral antiepileptic drugs medications (allopathic), the dose of medications increasing yearly and he was not satisfied with treatment, so he was advised with Panchakarma treatment starting from Deepana, Pachana, Vamana (medicated emesis), Virechana (medicated purgation), Basti (medicated enema), Shirodhara along with palliative treatment. After each treatment it was observed that the patient was satisfied with treatment and the complaints of seizure attack reduced in frequency and duration with improved quality of life. Palliative treatment was advised to continue along with modern medications.

scholarly journals Characteristics Predicting the Outcome in Individuals with Diabetic Foot Ulcerations

2017 ◽  
Vol 107 (3) ◽  
pp. 180-191 ◽  
Author(s):  
Lourdes Vella ◽  
Cynthia Formosa
Keyword(s):  
Ulcer Healing ◽  
Diabetic Foot ◽  
Health Care Professionals ◽  
Hemoglobin A1c ◽  
Foot Ulcer ◽  
Patient Characteristics ◽  
Outpatient Setting ◽  
Foot Complications

Background: We sought to determine patient and ulcer characteristics that predict wound healing in patients living with diabetes. Methods: A prospective observational study was conducted on 99 patients presenting with diabetic foot ulceration. Patient and ulcer characteristics were recorded. Patients were followed up for a maximum of 1 year. Results: After 1 year of follow-up, ulcer characteristics were more predictive of ulcer healing than were patient characteristics. Seventy-seven percent of ulcers had healed and 23% had not healed. Independent predictors of nonhealing were ulcer stage (P = .003), presence of biofilm (P = .020), and ulcer depth (P = .028). Although this study demonstrated that the baseline hemoglobin A1c reading at the start of the study was not a significant predictor of foot ulcer outcome (P = .603, resolved versus amputated), on further statistical analyses, when hemoglobin A1c was compared with the time taken for complete ulcer healing (n = 77), it proved to be significant (P = .009). Conclusions: The factors influencing healing are ulcer stage, presence of biofilm, and ulcer depth. These findings have important implications for clinical practice, especially in an outpatient setting. Prediction of outcome may be helpful for health-care professionals in individualizing and optimizing clinical assessment and management of patients. Identification of determinants of outcome could result in improved health outcomes, improved quality of life, and fewer diabetes-related foot complications.

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashutosh Shankhdhar ◽  
Pawan Kumar Verma ◽  
Prateek Agrawal ◽  
Vishu Madaan ◽  
Charu Gupta
Keyword(s):  
Human Brain ◽  
Network Architecture ◽  
Neural Control ◽  
Human Life ◽  
Binary Classification ◽  
Sampling Rate ◽  
Quality Analysis ◽  
Eeg Signal ◽  
Content Type

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

Fifteen-minute consultation: The dizzy child

2021 ◽  
pp. edpract-2020-320569
Author(s):  
Rosa Crunkhorn ◽  
Suparna Dasgupta ◽  
Arnab Kumar Seal ◽  
Soumit Dasgupta
Keyword(s):  
Quality Of Life ◽  
Positive Impact ◽  
Outpatient Setting ◽  
Vestibular Disorders ◽  
Significant Morbidity ◽  
Holistic Management ◽  
Paediatric Outpatient ◽  
Vestibular Pathology ◽  
Structured Approach

Vestibular disorders are often overlooked in children and may cause significant morbidity. About a third of children presenting with problems in balance show a vestibular pathology and the overall prevalence of paediatric vertigo is about 5%. Appropriate diagnosis and holistic management can have a significantly positive impact on a child’s quality of life and can be very rewarding. We present a structured approach to the assessment and management of a child presenting with dizziness in a general, non-neurological specialty or community paediatric outpatient setting.

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