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Huang Y, Zhao Y, Capstick A, Palermo F, Haddadi H, Barnaghi P. Analyzing entropy features in time-series data for pattern recognition in neurological conditions. Artif Intell Med 2024; 150:102821. [PMID: 38553161 DOI: 10.1016/j.artmed.2024.102821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon's entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline's effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.
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Affiliation(s)
- Yushan Huang
- Dyson School of Design Engineering, Imperial College London, London, UK; Great Ormond Street Hospital for Children, London, UK
| | - Yuchen Zhao
- Department of Computer Science, University of York, York, UK
| | - Alexander Capstick
- Department of Brain Sciences, Imperial College London, London, UK; Great Ormond Street Hospital for Children, London, UK
| | - Francesca Palermo
- Department of Brain Sciences, Imperial College London, London, UK; Great Ormond Street Hospital for Children, London, UK
| | - Hamed Haddadi
- Department of Computing, Imperial College London, London, UK
| | - Payam Barnaghi
- Department of Brain Sciences, Imperial College London, London, UK; The Great Ormond Street Institute of Child Health, University College London, London, UK; Great Ormond Street Hospital for Children, London, UK; Care Research and Technology Centre, The UK Dementia Research Institute, London, UK.
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Capstick A, Palermo F, Zakka K, Fletcher-Lloyd N, Walsh C, Cui T, Kouchaki S, Jackson R, Tran M, Crone M, Jensen K, Freemont P, Vaidyanathan R, Kolanko M, True J, Daniels S, Wingfield D, Nilforooshan R, Barnaghi P. Digital remote monitoring for screening and early detection of urinary tract infections. NPJ Digit Med 2024; 7:11. [PMID: 38218738 PMCID: PMC10787784 DOI: 10.1038/s41746-023-00995-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/11/2023] [Indexed: 01/15/2024] Open
Abstract
Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.
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Affiliation(s)
- Alexander Capstick
- Imperial College London, London, UK.
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK.
| | - Francesca Palermo
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Kimberley Zakka
- University College London, London, UK
- Great Ormond Street Hospital NHS Foundation Trust, London, UK
| | - Nan Fletcher-Lloyd
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Chloe Walsh
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Tianyu Cui
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Samaneh Kouchaki
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
- University of Surrey, London, UK
| | - Raphaella Jackson
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Martin Tran
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Michael Crone
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Kirsten Jensen
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Paul Freemont
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Ravi Vaidyanathan
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Magdalena Kolanko
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Jessica True
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
- Surrey and Borders Partnership NHS Foundation Trust, Leatherhead, UK
| | - Sarah Daniels
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - David Wingfield
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Ramin Nilforooshan
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
- University of Surrey, London, UK
- Surrey and Borders Partnership NHS Foundation Trust, Leatherhead, UK
| | - Payam Barnaghi
- Imperial College London, London, UK.
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK.
- University College London, London, UK.
- Great Ormond Street Hospital NHS Foundation Trust, London, UK.
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Palermo F, Chen Y, Capstick A, Fletcher-Loyd N, Walsh C, Kouchaki S, True J, Balazikova O, Soreq E, Scott G, Rostill H, Nilforooshan R, Barnaghi P. TIHM: An open dataset for remote healthcare monitoring in dementia. Sci Data 2023; 10:606. [PMID: 37689815 PMCID: PMC10492790 DOI: 10.1038/s41597-023-02519-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal, observational, and measurement data, within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days.
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Affiliation(s)
- Francesca Palermo
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Yu Chen
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Alexander Capstick
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Nan Fletcher-Loyd
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Chloe Walsh
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Samaneh Kouchaki
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- University of Surrey, Guildford, GU2 7XH, UK
| | - Jessica True
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Olga Balazikova
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Eyal Soreq
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Gregory Scott
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
| | - Helen Rostill
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Ramin Nilforooshan
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK
- Surrey and Borders Partnership NHS Trust, Leatherhead, KT22 7AD, UK
| | - Payam Barnaghi
- Imperial College London, Department of Brain Sciences, London, W12 0NN, UK.
- The UK Dementia Research Institute, Care Research and Technology Centre, London, W1T 7NF, UK.
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Quayle JA, Capstick A, Morris AI, Billington D. Plasma alkaline phosphodiesterase I in intrahepatic cholestasis induced by alpha-naphthylisothiocyanate in rats. Clin Sci (Lond) 1988; 75:13-20. [PMID: 2842102 DOI: 10.1042/cs0750013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
1. Administration of alpha-naphthylisothiocyanate (ANIT) to rats produced dose-dependent increases in plasma bile acid and bilirubin concentrations. Similar increases in plasma bile acid and bilirubin concentrations were evident in bile duct ligated rats, indicating that the severity of cholestasis is almost identical in both models. 2. Plasma alkaline phosphodiesterase I was increased by only 50-80% while alkaline phosphatase was increased more than threefold after ANIT administration. This is in contrast to an earlier study [S. R. Simpson, K. Rahman & D. Billington (1984) Clinical Science 67, 647-652] where, after bile duct ligation, serum alkaline phosphodiesterase I was elevated sixfold before any increase in alkaline phosphatase activity became apparent. Thus, plasma alkaline phosphodiesterase I does not offer as sensitive a marker of intrahepatic cholestasis (induced by ANIT) as it does of extrahepatic cholestasis (induced by bile duct ligation). 3. Hepatic alkaline phosphodiesterase I was unaffected by ANIT pretreatment while hepatic alkaline phosphatase was increased up to seven times. It is suggested that raised plasma alkaline phosphodiesterase I is due to regurgitation of the biliary enzyme rather than overspill of the enzyme from liver into blood. 4. Gel filtration showed that 24 h and 96 h after ANIT administration, rat serum contained a high molecular weight form of alkaline phosphodiesterase I, suggesting a different isoenzyme profile.
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Affiliation(s)
- J A Quayle
- Department of Chemistry and Biochemistry, Liverpool Polytechnic, U.K
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