Sensitivity and specificity of the morphology detection algorithm alone or combined with other algorithms for enhanced diagnostics

Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling). Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

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Evaluation of the device independent nature of a photoplethysmography-deriving smartphone app

EP Europace ◽

10.1093/europace/euab116.525 ◽

2021 ◽

Vol 23 (Supplement_3) ◽

Author(s):

H Gruwez ◽

S Evens ◽

T Proesmans ◽

C Smeets ◽

P Haemers ◽

...

Keyword(s):

Atrial Flutter ◽

Sensitivity And Specificity ◽

Heart Rhythm ◽

Smartphone Application ◽

Detection Algorithm ◽

Interquartile Range ◽

Detection Algorithms ◽

Funding Sources ◽

Successful Measurement ◽

Sensitivity Specificity

Abstract Funding Acknowledgements Type of funding sources: None. Background Smartphone apps using photoplethysmography (PPG) technology enable digital heart rhythm monitoring through their built-in camera, without the need for additional, specific, or costly hardware. This may positively impact the availability and scalability of remote monitoring. However, the diversity of smartphone specifications on the consumer market may raise concerns regarding the robustness of AF detection algorithms between various devices. Purpose To study the device independency of AF detection performance by a PPG-based smartphone application. Methods Patients from the cardiology department were consecutively enrolled. Patients were handed 7 iOS models and 1 Android model and were asked to consecutively perform one PPG measurement per device. A 12-lead electrocardiogram (ECG) was collected during the same consultation and interpreted by a cardiologist as reference diagnosis. To allow an objective comparison across the devices, patients who failed to perform one successful measurement on each device were excluded. Additional exclusions were atrial flutter rhythms and insufficient quality results. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was used for the head-to-head comparison of the sensitivity and specificity of the proprietary algorithm on the different smartphone devices. Results A total of 150 patients participated in the study with a median CHA2DS2-VASc score of 3 (interquartile range: 1-5). The median age of the study population was 70 (interquartile range: 56-79) years. In total, 54.7% of the population was male and the AF-prevalence was 35.3%. After the exclusion of patients with atrial flutter (n = 14) and patients who did not successfully perform a PPG measurement on each device (n = 5), diagnostic-grade results of 131 patients were used to calculate the performance of the proprietary algorithm. The sensitivity and specificity of the AF detection algorithm ranged from 90.9% (95% CI 75.7-98.1) to 100.0% (95% CI 91.0-100) and 94.5% (95% CI 86.6-98.5) to 100.0% (95% CI 94.6-100), respectively. The overall accuracy across the devices ranged from 94.4% (95% CI 88.3-97.9) to 99.0% (95% CI 94.6-100). Head-to-head comparisons of the results did not reveal significant differences in sensitivity (P = 0.125-1.000) or specificity (P = 0.375-1.000) of the proprietary AF detection algorithm among the different devices. Conclusion This study demonstrated the device-independent nature of the PPG-deriving smartphone application with respect to 12-lead ECG diagnosis.

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The Use of Nonlinear Parameters of Heart Rate Variability for Stress Detection

Journal of Medical and Biological Research ◽

10.37482/2687-1491-z064 ◽

2021 ◽

pp. 265-274

Author(s):

Dmitriy Dimitriev ◽

◽

Elena Saperova ◽

Aleksey Dimitriev ◽

El’dar Salimov ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Sensitivity And Specificity ◽

Recurrence Plot ◽

Line Length ◽

Detection Algorithm ◽

Stress Detection ◽

Linear Discriminant ◽

Rr Interval ◽

Diagonal Line

This paper presents a stress detection algorithm using heart rate variability (HRV) parameters. Five-minute electrocardiograms were recorded at rest and under exam stress (252 students were involved). The determined HRV parameters were applied to detect stress by means of several classification algorithms. We analysed linear indices in the time (standard deviation of NN intervals (SDNN) and root mean square of successive RR interval differences (RMSSD)) and frequency domains (low frequency (LF) and high frequency (HF) power as well as LF/HF ratio). To study nonlinear HRV indices, we evaluated approximate entropy (ApEn), sample entropy (SampEn), α1 (DFA1) and α2 (DFA2) scaling exponents, correlation dimension D2, and recurrence plot quantification measures (recurrence rate (REC), mean diagonal line length (Lmean), maximum diagonal line length (Lmax), determinism (DET), and Shannon entropy (ShanEn)). Receiver operating characteristic (ROC) was used to test the performance of the classifiers derived from HRV. The highest area under the ROC curve (AUC), sensitivity, and specificity were found for mean RR-interval, DFA1, DFA2, RMSSD, and Lmax. These parameters were used for stress/rest classification with the help of algorithms that are common in clinical and physiological applications, i.e. logistic regression (LR) and linear discriminant analysis (LDA). Classification performance for stress was quantified using accuracy, sensitivity and specificity measures. The LR achieved an accuracy of 68.25 % at an optimal cutoff value of 0.57. LDA determined stress with 67.46 % accuracy. Thus, HRV parameters can serve as an objective tool for stress detection.

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Sensitivity and specificity of the morphology detection algorithm alone or combined with other algorithms for enhanced diagnostics

EP Europace ◽

10.1016/eupace/2.supplement_1.a47-c ◽

2001 ◽

Vol 2 (Supplement_1) ◽

pp. A47-A47

Author(s):

G. Boriani ◽

E. Occhetta ◽

G. Pistis ◽

C. Menozzi ◽

M Jorfida ◽

...

Keyword(s):

Sensitivity And Specificity ◽

Detection Algorithm

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TO STUDY CORNEAL BIOMECHANICS USING CORVIS ST IN NORMALAND KERATOCONUS CORNEAS

10.36106/ijsr/4504261 ◽

2020 ◽

pp. 1-4

Author(s):

Choksi Twinkle M ◽

Shetty Rohit ◽

Sahdev Saroj I

Keyword(s):

Sensitivity And Specificity ◽

Predictive Accuracy ◽

Standard Technique ◽

High Sensitivity ◽

Detection Algorithm ◽

Corvis St ◽

Logistic Regression Models ◽

Multiple Parameters ◽

Set Up ◽

Cost Parameters

Aim: To compare various parameters derived during Corvis ST (CoST) measurement in normal, forme fruste keratoconus (FFKC) and keratoconus (KC) subjects. Methods: 102 eyes of 79 participants of which 43 eyes from 43 age-matched controls, 19 eyes of 15 FFKC patients and 40 eyes from 27 KC patients were included in the study. Standard technique for measurement by CoST was followed and advanced edge detection algorithm was applied to derive multiple parameters. Receiver operating curves (ROC) were set up to separately identify the predictive accuracy of the various CoST parameters to detect FFKC and KC compared to normal eyes and presented as area under the ROC (AUROC) along with its standard error and 95% CI. Results: The mean age of the participants was 25.5 + 8.3 years (median= 25 years, IQR= 21 – 29 years, range= 11-54 years) and 50 (63%) were men. In univariate ordinal and logistic regression models, increment in HCDA and A2T were found to significantly increase the likelihood of KC while increase in HCR, A1T and A2V were associated with lower likelihood of a diagnosis of KC. There were no statistically significant differences between normal eyes and those with FFKC in terms of the CoST parameters. An HCR value of < 6.02mm had the highest AUROC and showed a very high sensitivity and specificity for differentiating KC from normal eyes. Conclusion: Five CoST parameters, viz. deflection amplitude, highest concavity radius, first and second applanation time and applanation velocity at second moment showed high sensitivity and specificity in differentiating normal from KC eyes. Highest concavity radius was found to be most sensitive and specific for differentiating KC from normal corneas.

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Applying Item Response Theory Modeling to Identify Social (Pragmatic) Communication Disorder

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-19-00284 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1916-1932 ◽

Cited By ~ 1

Author(s):

Haiying Yuan ◽

Christine Dollaghan

Keyword(s):

Item Response Theory ◽

Item Response ◽

Sensitivity And Specificity ◽

Social Communication ◽

National Database ◽

Communication Disorder ◽

Social Communication Questionnaire ◽

Autism Research ◽

The Social ◽

Pragmatic Communication

Purpose No diagnostic tools exist for identifying social (pragmatic) communication disorder (SPCD), a new Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition category for individuals with social communication deficits but not the repetitive, restricted behaviors and interests (RRBIs) that would qualify them for a diagnosis of autism spectrum disorder (ASD). We explored the value of items from a widely used screening measure of ASD for distinguishing SPCD from typical controls (TC; Aim 1) and from ASD (Aim 2). Method We applied item response theory (IRT) modeling to Social Communication Questionnaire–Lifetime ( Rutter, Bailey, & Lord, 2003 ) records available in the National Database for Autism Research. We defined records from putative SPCD ( n = 54), ASD ( n = 278), and TC ( n = 274) groups retrospectively, based on National Database for Autism Research classifications and Autism Diagnostic Interview–Revised responses. After assessing model assumptions, estimating model parameters, and measuring model fit, we identified items in the social communication and RRBI domains that were maximally informative in differentiating the groups. Results IRT modeling identified a set of seven social communication items that distinguished SPCD from TC with sensitivity and specificity > 80%. A set of five RRBI items was less successful in distinguishing SPCD from ASD (sensitivity and specificity < 70%). Conclusion The IRT modeling approach and the Social Communication Questionnaire–Lifetime item sets it identified may be useful in efforts to construct screening and diagnostic measures for SPCD.

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Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

American Journal of Speech-Language Pathology ◽

10.1044/2019_ajslp-18-0181 ◽

2019 ◽

Vol 28 (3) ◽

pp. 1257-1267 ◽

Cited By ~ 2

Author(s):

Priya Kucheria ◽

McKay Moore Sohlberg ◽

Jason Prideaux ◽

Stephen Fickas

Keyword(s):

Academic Success ◽

Undergraduate Students ◽

Expository Text ◽

Reading Strategy ◽

Detection Algorithm ◽

Expository Texts ◽

Human Observer ◽

Behavioral Strategies ◽

Periodic Review ◽

Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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