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Bonet-Luz E, Nandi M, Christie MI, Doyle J, Pierson JB, Pugsley MK, Vargas HM, Aston PJ. Detection of contractility changes in the heart from arterial blood pressure data using symmetric projection attractor reconstruction. J Pharmacol Toxicol Methods 2024:107546. [PMID: 39069108 DOI: 10.1016/j.vascn.2024.107546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/24/2024] [Accepted: 07/25/2024] [Indexed: 07/30/2024]
Abstract
The potential for unintended drug induced changes in cardiac contractility is a major concern in medicines development. Whilst direct left ventricular pressure (LVP) measurement is the gold standard for measuring cardiac contractility in vivo, it is resource intensive and poses a welfare burden on research animals. In contrast, arterial blood pressure (BP) measurement has fewer challenges. Symmetric Projection Attractor Reconstruction (SPAR) is a signal processing technique which transforms physiological time-series signals into a corresponding visual image ('attractor'), amplifying morphology changes within physiological waveforms. It was hypothesized that SPAR analysis of BP signals would provide a surrogate measure of cardiac contractility by specifically amplifying the maximum slope of the systolic upstroke. BP (abdominal aorta) signals obtained from beagle dogs, treated with positive and negative inotropes, were retrospectively analysed to identify signal features that correlated with the maximum upslope of the LVP signal from simultaneously acquired LVP recordings. SPAR transformation of BP signals quantified drug induced changes in the maximum slope of the systolic upstroke. We identified key SPAR metrics that provided >0.8 correlation with the LVP maximum upslope, outperforming the BP systolic upstroke alone. This was observed for all 4 different drugs, doses and time points evaluated across studies. Thus, we conclude that the SPAR measures derived from the BP signal could be used as a first pass in vivo screen to flag any risk of drug induced changes in cardiac contractility during the conduct of non-clinical medicines development, potentially reducing or replacing the need to perform direct left ventricular measurements.
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Affiliation(s)
- Esther Bonet-Luz
- Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, UK
| | - Manasi Nandi
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, Stamford Street, London SE1 9NH, UK.
| | - Mark I Christie
- Akranim Ltd., 90/92 King Street, Maidstone, Kent ME14 1BH, UK.
| | | | | | - Michael K Pugsley
- Department of Toxicology & Safety Pharmacology, Cytokinetics, LLC, South San Francisco, CA 94080, USA.
| | - Hugo M Vargas
- Translational Safety & Bioanalytical Sciences, Amgen, Inc., Thousand Oaks, CA 91320, USA.
| | - Philip J Aston
- Department of Mathematics, University of Surrey, Guildford, Surrey GU2 7XH, UK.
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Pierson JB, Berridge B, Blinova K, Brooks MB, Eldridge S, O'Brien CE, Pugsley MK, Schultze AE, Smith G, Stockbridge N, Valentin JP, Vicente J. Collaborative science in action: A 20 year perspective from the Health and Environmental Sciences Institute (HESI) Cardiac Safety Committee. J Pharmacol Toxicol Methods 2024; 127:107511. [PMID: 38710237 DOI: 10.1016/j.vascn.2024.107511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024]
Abstract
The Health and Environmental Sciences Institute (HESI) is a nonprofit organization dedicated to resolving global health challenges through collaborative scientific efforts across academia, regulatory authorities and the private sector. Collaborative science across non-clinical disciplines offers an important keystone to accelerate the development of safer and more effective medicines. HESI works to address complex challenges by leveraging diverse subject-matter expertise across sectors offering access to resources, data and shared knowledge. In 2008, the HESI Cardiac Safety Committee (CSC) was established to improve public health by reducing unanticipated cardiovascular (CV)-related adverse effects from pharmaceuticals or chemicals. The committee continues to significantly impact the field of CV safety by bringing together experts from across sectors to address challenges of detecting and predicting adverse cardiac outcomes. Committee members have collaborated on the organization, management and publication of prospective studies, retrospective analyses, workshops, and symposia resulting in 38 peer reviewed manuscripts. Without this collaboration these manuscripts would not have been published. Through their work, the CSC is actively addressing challenges and opportunities in detecting potential cardiac failure modes using in vivo, in vitro and in silico models, with the aim of facilitating drug development and improving study design. By examining past successes and future prospects of the CSC, this manuscript sheds light on how the consortium's multifaceted approach not only addresses current challenges in detecting potential cardiac failure modes but also paves the way for enhanced drug development and study design methodologies. Further, exploring future opportunities and challenges will focus on improving the translational predictability of nonclinical evaluations and reducing reliance on animal research in CV safety assessments.
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Affiliation(s)
| | | | | | - Marjory B Brooks
- Comparative Coagulation Section, Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY, USA
| | - Sandy Eldridge
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Claire E O'Brien
- Health and Environmental Sciences Institute, Washington, DC, USA.
| | - Michael K Pugsley
- Toxicology & Safety Pharmacology, Cytokinetics, South San Francisco, CA, USA
| | - A Eric Schultze
- Pathology, Lilly Research Laboratories, Indianapolis, IN, USA
| | - Godfrey Smith
- Clyde Biosciences Ltd, Newhouse, UK; University of Glasgow, Scotland, UK
| | | | - Jean-Pierre Valentin
- UCB Biopharma SRL, Development Science, Non-Clinical Safety Evaluation, Braine l'Alleud, Belgium
| | - Jose Vicente
- Food and Drug Administration, Silver Spring, MD, USA
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Mathieu AJW, Pascual MS, Charlton PH, Volovaya M, Venton J, Aston PJ, Nandi M, Alastruey J. Advanced waveform analysis of the photoplethysmogram signal using complementary signal processing techniques for the extraction of biomarkers of cardiovascular function. JRSM Cardiovasc Dis 2024; 13:20480040231225384. [PMID: 38314325 PMCID: PMC10838030 DOI: 10.1177/20480040231225384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/08/2023] [Accepted: 12/09/2023] [Indexed: 02/06/2024] Open
Abstract
Introduction Photoplethysmogram signals from wearable devices typically measure heart rate and blood oxygen saturation, but contain a wealth of additional information about the cardiovascular system. In this study, we compared two signal-processing techniques: fiducial point analysis and Symmetric Projection Attractor Reconstruction, on their ability to extract new cardiovascular information from a photoplethysmogram signal. The aim was to identify fiducial point analysis and Symmetric Projection Attractor Reconstruction indices that could classify photoplethysmogram signals, according to age, sex and physical activity. Methods Three datasets were used: an in-silico dataset of simulated photoplethysmogram waves for healthy male participants (25-75 years old); an in-vivo dataset containing 10-min photoplethysmogram recordings from 57 healthy subjects at rest (18-39 or > 70 years old; 53% female); and an in-vivo dataset containing photoplethysmogram recordings collected for 4 weeks from a single subject, in daily life. The best-performing indices from the in-silico study (5/48 fiducial point analysis and 6/49 Symmetric Projection Attractor Reconstruction) were applied to the in-vivo datasets. Results Key fiducial point analysis and Symmetric Projection Attractor Reconstruction indices, which showed the greatest differences between groups, were found to be consistent across datasets. These indices were related to systolic augmentation, diastolic peak positioning and prominence, and waveform variability. Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques provided indices that supported the classification of age and physical activity, but not sex. Conclusions Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques demonstrated utility in identifying cardiovascular differences between individuals and within an individual over time. Future research should investigate the potential utility of these techniques for extracting information on fitness and disease, to support healthcare-decision making.
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Affiliation(s)
- Aristide Jun Wen Mathieu
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital, London, UK
| | - Miquel Serna Pascual
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Maria Volovaya
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Jenny Venton
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Philip J Aston
- Department of Mathematics, University of Surrey, Guildford, UK
| | - Manasi Nandi
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital, London, UK
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Ansari MY, Qaraqe M, Charafeddine F, Serpedin E, Righetti R, Qaraqe K. Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade. Artif Intell Med 2023; 146:102690. [PMID: 38042607 DOI: 10.1016/j.artmed.2023.102690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 12/04/2023]
Abstract
Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.
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Affiliation(s)
- Mohammed Yusuf Ansari
- Texas A&M University, College Station, TX, USA; Texas A&M University at Qatar, Doha, Qatar.
| | - Marwa Qaraqe
- Division of Information and Computing Technology, Hamad Bin Khalifa University, Doha, Qatar; Texas A&M University at Qatar, Doha, Qatar
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Serna-Pascual M, D'Cruz RF, Volovaya M, Jolley CJ, Hart N, Rafferty GF, Steier J, Aston PJ, Nandi M. Novel breathing pattern analysis: Symmetric Projection Attractor Reconstruction improves identification of impending COPD re-exacerbations - a retrospective cohort analysis. ERJ Open Res 2023; 9:00164-2023. [PMID: 37650090 PMCID: PMC10463025 DOI: 10.1183/23120541.00164-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/05/2023] [Indexed: 09/01/2023] Open
Abstract
Respiratory waveforms can be reduced to simple metrics, such as rate, but this may miss information about waveform shape and whole breathing pattern. A novel analysis method quantifying the whole waveform shape identifies AECOPD earlier. https://bit.ly/3M6uIEB.
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Affiliation(s)
- Miquel Serna-Pascual
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
- These authors contributed equally
| | - Rebecca F. D'Cruz
- Lane Fox Clinical Respiratory Physiology Research Unit, Guy's and St Thomas’ NHS Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
- These authors contributed equally
| | - Maria Volovaya
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Caroline J. Jolley
- Centre for Human and Applied Physiological Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Nicholas Hart
- Lane Fox Clinical Respiratory Physiology Research Unit, Guy's and St Thomas’ NHS Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Gerrard F. Rafferty
- Centre for Human and Applied Physiological Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Joerg Steier
- Lane Fox Clinical Respiratory Physiology Research Unit, Guy's and St Thomas’ NHS Foundation Trust, London, UK
- Centre for Human and Applied Physiological Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Philip J. Aston
- Department of Mathematics, University of Surrey, Guildford, UK
| | - Manasi Nandi
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
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Sex Recognition through ECG Signals aiming toward Smartphone Authentication. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Physiological signals are strongly related to a person’s state of health and carry information about the human body. For example, by ECG, it is possible to obtain information about cardiac disease, emotions, personal identification, and the sex of a person, among others. This paper proposes the study of the heartbeat from a soft-biometric perspective to be applied to smartphone unlocking services. We employ the user heartbeat to classify the individual by sex (male, female) with the use of Deep Learning, reaching an accuracy of 94.4% ± 2.0%. This result was obtained with the RGB representation of the union of the time-frequency transformation from the pseudo-orthogonal X, Y, and Z bipolar signals. Evaluating each bipolar contribution, we found that the XYZ combination provides the best category distinction using GoogLeNet. The 24-h Holter database of the study contains 202 subjects with a female size of 49.5%. We propose an architecture for managing this signal that allows the use of a few samples to train the network. Due to the hidden nature of ECG, it does not present vulnerabilities like public trait exposition, light/noise sensibility, or learnability compared to fingerprint, facial, voice, or password verification methods. ECG may complement those gaps en route to a cooperative authentication ecosystem.
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Huang YH, Lyle JV, Razak ASA, Nandi M, Marr CM, Huang CLH, Aston PJ, Jeevaratnam K. Detecting Paroxysmal Atrial Fibrillation from Normal Sinus Rhythm in Equine Athletes using Symmetric Projection Attractor Reconstruction and Machine Learning. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:96-106. [PMID: 35493267 PMCID: PMC9043370 DOI: 10.1016/j.cvdhj.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Nandi M, Anton M, Lyle JV. Cardiovascular waveforms - can we extract more from routine signals? JRSM Cardiovasc Dis 2022; 11:20480040221121438. [PMID: 36092374 PMCID: PMC9459482 DOI: 10.1177/20480040221121438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular waveforms such as blood pressure, ECG and photoplethysmography (PPG), are routinely acquired by specialised monitoring devices. Such devices include bedside monitors, wearables and radiotelemetry which sample at very high fidelity, yet most of this numerical data is disregarded and focus tends to reside on single point averages such as the maxima, minima, amplitude, rate and intervals. Whilst, these measures are undoubtedly of value, we may be missing important information by simplifying the complex waveform signal in this way. This Special Collection showcases recent advances in the appraisal of routine signals. Ultimately, such approaches and technologies may assist in improving the accuracy and sensitivity of detecting physiological change. This, in turn, may assist with identifying efficacy or safety signals for investigational new drugs or aidpatient diagnosis and management, supporting scientific and clinical decision making.
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Affiliation(s)
- Manasi Nandi
- Reader in integrative pharmacology, School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Mary Anton
- NIHR pre-doctoral nursing fellow, Royal Brompton Hospital (paediatric intensive care), London, UK
| | - Jane V Lyle
- Department of Mathematics, University of Surrey, Guildford, UK
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Lyle JV, Aston PJ. Symmetric projection attractor reconstruction: Embedding in higher dimensions. CHAOS (WOODBURY, N.Y.) 2021; 31:113135. [PMID: 34881593 DOI: 10.1063/5.0064450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/18/2021] [Indexed: 06/13/2023]
Abstract
Symmetric Projection Attractor Reconstruction (SPAR) provides an intuitive visualization and simple quantification of the morphology and variability of approximately periodic signals. The original method takes a three-dimensional delay coordinate embedding of a signal and subsequently projects this phase space reconstruction to a two-dimensional image with threefold symmetry, providing a bounded visualization of the waveform. We present an extension of the original work to apply delay coordinate embedding in any dimension N≥3 while still deriving a two-dimensional output with some rotational symmetry property that provides a meaningful visualization of the higher dimensional attractor. A generalized result is developed for taking N≥3 delay coordinates from a continuous periodic signal, where we determine invariant subspaces of the phase space that provide a two-dimensional projection with the required rotational symmetry. The result in each subspace is shown to be equivalent to following each pair of coefficients of the trigonometric interpolating polynomial of N evenly spaced points as the signal is translated horizontally. Bounds on the mean and the frequency response of our new coordinates are derived. We demonstrate how this aids our understanding of the attractor properties and its relationship to the underlying waveform. Our generalized result is then extended to real, approximately periodic signals, where we demonstrate that the higher dimensional SPAR method provides information on subtle changes in different parts of the waveform morphology.
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Affiliation(s)
- J V Lyle
- Department of Mathematics, University of Surrey, Guildford GU2 7XH, United Kingdom
| | - P J Aston
- Department of Mathematics, University of Surrey, Guildford GU2 7XH, United Kingdom
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