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Reichmann H, Klingelhoefer L, Bendig J. The use of wearables for the diagnosis and treatment of Parkinson's disease. J Neural Transm (Vienna) 2023; 130:783-791. [PMID: 36609737 PMCID: PMC10199831 DOI: 10.1007/s00702-022-02575-5] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 12/13/2022] [Indexed: 01/09/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population.
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Affiliation(s)
- Heinz Reichmann
- Department of Neurology, University Hospital Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Lisa Klingelhoefer
- Department of Neurology, University Hospital Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Jonas Bendig
- Department of Neurology, University Hospital Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
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Sharma A, Feng L, Muresanu DF, Tian ZR, Lafuente JV, Buzoianu AD, Nozari A, Bryukhovetskiy I, Manzhulo I, Wiklund L, Sharma HS. Nanowired Delivery of Cerebrolysin Together with Antibodies to Amyloid Beta Peptide, Phosphorylated Tau, and Tumor Necrosis Factor Alpha Induces Superior Neuroprotection in Alzheimer's Disease Brain Pathology Exacerbated by Sleep Deprivation. Adv Neurobiol 2023; 32:3-53. [PMID: 37480458 DOI: 10.1007/978-3-031-32997-5_1] [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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Sleep deprivation induces amyloid beta peptide and phosphorylated tau deposits in the brain and cerebrospinal fluid together with altered serotonin metabolism. Thus, it is likely that sleep deprivation is one of the predisposing factors in precipitating Alzheimer's disease (AD) brain pathology. Our previous studies indicate significant brain pathology following sleep deprivation or AD. Keeping these views in consideration in this review, nanodelivery of monoclonal antibodies to amyloid beta peptide (AβP), phosphorylated tau (p-tau), and tumor necrosis factor alpha (TNF-α) in sleep deprivation-induced AD is discussed based on our own investigations. Our results suggest that nanowired delivery of monoclonal antibodies to AβP with p-tau and TNF-α induces superior neuroprotection in AD caused by sleep deprivation, not reported earlier.
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Affiliation(s)
- Aruna Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden
| | - Lianyuan Feng
- Department of Neurology, Bethune International Peace Hospital, Shijiazhuang, Hebei Province, China
| | - Dafin F Muresanu
- Department Clinical Neurosciences, University of Medicine & Pharmacy, Cluj-Napoca, Romania
- "RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Z Ryan Tian
- Department Chemistry & Biochemistry, University of Arkansas, Fayetteville, AR, USA
| | - José Vicente Lafuente
- LaNCE, Department Neuroscience, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
| | - Anca D Buzoianu
- Department of Clinical Pharmacology and Toxicology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ala Nozari
- Anesthesiology & Intensive Care, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Igor Bryukhovetskiy
- Department of Fundamental Medicine, School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia
- Laboratory of Pharmacology, National Scientific Center of Marine Biology, Far East Branch of the Russian Academy of Sciences, Vladivostok, Russia
| | - Igor Manzhulo
- Laboratory of Pharmacology, National Scientific Center of Marine Biology, Far East Branch of the Russian Academy of Sciences, Vladivostok, Russia
| | - Lars Wiklund
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden
| | - Hari Shanker Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
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Ko YF, Kuo PH, Wang CF, Chen YJ, Chuang PC, Li SZ, Chen BW, Yang FC, Lo YC, Yang Y, Ro SCV, Jaw FS, Lin SH, Chen YY. Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease. Biosensors (Basel) 2022; 12:bios12020074. [PMID: 35200335 PMCID: PMC8869576 DOI: 10.3390/bios12020074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 05/15/2023]
Abstract
Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.
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Affiliation(s)
- Yi-Feng Ko
- Department of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan; (Y.-F.K.); (F.-S.J.)
| | - Pei-Hsin Kuo
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97002, Taiwan;
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Yu-Jen Chen
- Department of Healthcare Solution FW R&D, ASUSTeK Computer Incrporation, Taipei 11259, Taiwan; (Y.-J.C.); (P.-C.C.)
| | - Pei-Chi Chuang
- Department of Healthcare Solution FW R&D, ASUSTeK Computer Incrporation, Taipei 11259, Taiwan; (Y.-J.C.); (P.-C.C.)
| | - Shih-Zhang Li
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
| | - Fu-Chi Yang
- School of Health Care Administration, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yu-Chun Lo
- The Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yi Yang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
| | - Shuan-Chu Vina Ro
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
| | - Fu-Shan Jaw
- Department of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan; (Y.-F.K.); (F.-S.J.)
| | - Sheng-Huang Lin
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97002, Taiwan;
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
- Correspondence: (S.-H.L.); (Y.-Y.C.)
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
- The Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Correspondence: (S.-H.L.); (Y.-Y.C.)
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Kyritsis K, Fagerberg P, Ioakimidis I, Chaudhuri KR, Reichmann H, Klingelhoefer L, Delopoulos A. Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors. Sci Rep 2021; 11:1632. [PMID: 33452324 DOI: 10.1038/s41598-020-80394-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/14/2020] [Indexed: 02/06/2023] Open
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.
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San-Segundo R, Zhang A, Cebulla A, Panev S, Tabor G, Stebbins K, Massa RE, Whitford A, de la Torre F, Hodgins J. Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers. Sensors (Basel) 2020; 20:E5817. [PMID: 33066691 PMCID: PMC7602495 DOI: 10.3390/s20205817] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/27/2020] [Accepted: 10/05/2020] [Indexed: 12/18/2022]
Abstract
Continuous in-home monitoring of Parkinson's Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.
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Affiliation(s)
- Rubén San-Segundo
- Center for Information Processing and Telecommunications, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Ada Zhang
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Alexander Cebulla
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Stanislav Panev
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Griffin Tabor
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Katelyn Stebbins
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | | | - Andrew Whitford
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Fernando de la Torre
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Jessica Hodgins
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
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