Matteo Pastorino - Remote daily activity of parkinson’s disease patients the akinesia assessment
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Matteo Pastorino - Remote daily activity of parkinson’s disease patients the akinesia assessment



Presentation of Workshop on Technology for Healthcare and Healthy Lifestyle 2011

Presentation of Workshop on Technology for Healthcare and Healthy Lifestyle 2011

Thursday 1st Dec 2011
Session II



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Matteo Pastorino - Remote daily activity of parkinson’s disease patients the akinesia assessment Presentation Transcript

  • 1. Remote daily activity of Parkinson’s disease patients: the Akinesia assessment . Matteo Pastorino Technical University of Madrid WTHS_2011 Valencia, December 2011 WTHS_2011 | Valencia| December 2011 1
  • 2. Parkinson’s Disease (PD)Parkinson’s Disease (PD) is a degenerative,progressive disorder that affects nerve cells in deepparts of the brain called the basal ganglia and thesubstantia nigra. Nerve cells in the substantia nigra produce the neurotransmitter dopamine and are responsible for relaying messages that plan and control body movement. Causes: Genetic, environmental factors … WTHS_2011 | Valencia| December 2011 2
  • 3. Parkinson’s Disease (PD) - Symptoms Movement disorders  Bradykinesia  Akinesia  Rigidity  Tremor  Dyskinesia  Freezing of Gait Cognitive and behavioural disorders  Dementia  Depression  Hallucination Sensory, sleep and emotional problems WTHS_2011 | Valencia| December 2011 3
  • 4. Parkinson’s Disease (PD) - Akinesia Akinesia (α a, "absence", κίνησις kinēsis, “movement") represents the most promising motor progression marker of the disease.Characteristics: defined as absence of movement. This is a condition in which any automaticmovement or action, including gestures, blinking or swallowing actions are limited and theirfrequency decreases, although the elemental motor functions are maintained and can beperformed voluntarily. Various aspects appear to contribute to the self-initiation of movements:Causes: reduced dopaminergic input to the striatum.Such changes also cause bradykinesia, rigidity, tremor and postural instability, although the underlyingmechanisms leading to these symptoms are still not understood.Treatment: dopamine precursor levodopa is the most efficient treatment for the improvement ofParkinson´s disease signs and symptoms.However, abnormal involuntary movements (dyskinesia) are motor fluctuations that occur in the majority ofPD’s patients undergoing this treatment. WTHS_2011 | Valencia| December 2011 4
  • 5. Parkinson’s Disease (PD) – ONOFFSpecific Chronic Neurodegenerative Diseases Progressive loss of motion ability (due to muscle weakening) Appearance of new motion symptoms (new muscles affected) Inability to move (at later stages) Parkinson’s Disease:  Progression is restricted with treatment  Daily motion status is fluctuating due to treatment  Dyskinesia WTHS_2011 | Valencia| December 2011 5
  • 6. Monitoring & Assessment TODAY Every 4-6 months or As instructed Patient visits Clinic Treatment Adjustment Clinician tries to Clinician PERFORMs Made from visitsReconstruct the patient status UPDRS or other tests to identify observations & subjective Throughout day and night Current patient status assessment WTHS_2011 | Valencia| December 2011 6
  • 7. Parkinson Disease & p-Health solutions 24h Every day monitoring Patient at home ALERTS! Treatment AdjustmentTest Devices Based on objective observation Other Info 24 h objective status assessment Immediate Response WTHS_2011 | Valencia| December 2011 7
  • 8. PD & p-Health solutions: OBJECTIVESShort Term• 24h objective assessment of patient status• Detection of dosage wearing-off• Adjustment of therapy according to personal characteristics and reaction • Medication schedule/dosage • Food Intake• Detection of changes in patient reaction to therapy• All patient info at-a-glance and detailed info one-click awayLong Term• Objective therapy assessment• Analysis of symptoms progression in time• Recognition of changes in therapeutic response WTHS_2011 | Valencia| December 2011 8
  • 9. PERFORM Project This work is part of the PERFORM project, partially funded by the European Commission under the 7th framework programme Consortium: WTHS_2011 | Valencia| December 2011 9
  • 10. PERFORM Architecture 24h monitoring Other InfoTest Devices Monitor Detect & Quantify Patient Symptoms & Gait Build New Treatment Patient Specific Regime disease profile Suggest Assess Treatment Changes Disease Progress WTHS_2011 | Valencia| December 2011 10
  • 11. PERFORM Architecture Clinician Administrator Professional GUI Central Hospital Unit Local Base Unit Central Hospital Unit Login Manager User Database Account Manager  Exploits the recorded patient information in order to build a Alert Manager  Processes Patient GUI the patient signals patient symptom profile. acquired; Patient List Index of Processed Info Local Base Unit the targeted patient  Detects For each symptom symptoms (e.g.: tremor,  produces a patient profile which describes the patient’s Clinical Decision Support Systems Central Unit Information Handler levodopa induced dyskinesia, Device Controller Scheduler common symptom features. Central Unit Communicator Akinesia,..).  compared with thePatient Modelling patient symptom profile. Patient Management Information Handler For Processorsymptom a dedicated Test each Gait Early Wearing Off submodule : Action Tremor If significant differences are found, it might be due to two reasons:  Processes the relevant signals;  temporarily patient behaviour abnormalityMedication Change On – Off  Detects the symptomFreezing of Gait episode; or Stability-Worsening LID  the symptom episode:  a change in the patient profile. Gait  according to the Unified Tremor Parkinson’s Disease Tremor Bradykinesia MonitoringWearableSensors Logger Rating Scale or LID Frequent falls System  Other features Activity as such Checks whether a substantial number of similar situations are User- Hardware Interface duration, frequency, identified for the last time period for the specific patient and if Repository erergy and Bradykinesia amplitude that occurs, it creates an alert. Fall Detector Interoperability Manager might also be Akinesia provided Alert for further clinician PERFORM Manager review and Communicator system Repository evaluation. External Resources WTHS_2011 | Valencia| December 2011 11
  • 12. PERFORM Monitoring System Gyroscope/ Accelerometer AccelerometerDay Monitoring Accelerometer wearable Accelerometer / Control Unit Accelerometer Accelerometer WTHS_2011 | Valencia| December 2011 12
  • 13. PERFORM: Patient InterfaceInterface easy to useLook and feel of the phone dialling padDrag and Drop functionsUsed to declare subjective estimation of Patient statusUsed to receive instructions on life-style interventions(medication/food intake) WTHS_2011 | Valencia| December 2011 13
  • 14. PERFORM Technological Innovation Continuous Patient Monitoring & Assessment Detection of all symptoms using a single and low cost sensor setting Early recognition of disease progression and patient reaction changes Assistance in patient management with expert knowledge based systems Prognosis of disease evolution according to patient characteristics WTHS_2011 | Valencia| December 2011 14
  • 15. PERFORM: Pilots descriptionPhase Characteristics # of patients Objective Pilot Sites Data Collection 8 healthy + Design the 1 with SHIMMERS 8 PD Madrid and Pamplona(Spain) algorithm Data Collection in Design the Pamplona(Spain) and Ioannina 2 a supervised 20 algorithm and train (Greece) environment the classifier Data Collected in Pamplona(Spain) and Ioannina 3 a unsupervised 24 Data Collection (Greece) environment Data Collected in Test and Validation Modena (Italy) 4 a unsupervised 22 of algorithms environment WTHS_2011 | Valencia| December 2011 15
  • 16. Akinesia Algorithm and designDifferent modules were created in order to detect and quantify different symptoms AKINESIA module assesses the amount of movement of the patient in space for any given period of time. WTHS_2011 | Valencia| December 2011 16
  • 17. Akinesia: Algorithm design Output• Pre-processing: • Resultant Computation: eliminate position dependence of the sensors given by: • x2 + y2 + z2 • Filtering: Akinesia is related with the slowness of the movement ,therefore we are interested in the low frequencies of the signal: • Band-Pass IIR Butterworth 4th order filter [1÷3] Hz. WTHS_2011 | Valencia| December 2011 17
  • 18. Akinesia: Algorithm design OutputThe signal is split in 5 minutes length epochs to evaluate a considerable portion of signal. There is 50%overlapping in epochs to study the whole signal.For each epoch computes Total Amount of Energy for each working sensor. OUTPUT: The resulting energies of each sensor are combined by using a weighted sum in order to take into account all the possible combination of sensors. The module is able to recognize automatically the sensor’s setting WTHS_2011 | Valencia| December 2011 18
  • 19. Akinesia: ON-OFF Evaluation→ clear relationship between ON-OFF phases and the akinesia levels.→ Strong correlation between the lack of movement and OFF status.→Using the akinesia is possible to discriminate ON and OFF periods in PD patients. ON OFF ON WTHS_2011 | Valencia| December 2011 19
  • 20. Akinesia: Results For the analysis of the results, two different scenarios are considered. Akinesia – NO WALKING periods Akinesia – WALKING periods mean value of the computed akinesia during the  mean value of the computed akinesia during periods when the patient is not walking. walking periods. The global evaluation of both scenarios demonstrates that it is possible to discriminate ON and OFF periods computing the lack of movement combining the information provided by different modules of the PERFORM system, in this case the activity recognizer and the Akinesia module. WTHS_2011 | Valencia| December 2011 20
  • 21. Conclusions Useful and Objective tool for the analysis of the akinesia in PD’s patients. Suitable for clinical practice Support health professionals in the diagnosis and follow- up of PD patients PD patients’ quality of life improvement Discriminating parameter for the ON – OFF condition DATA: Recording of one patient during 4h and are focused only in the akinesia results as discriminating parameter for the ON – OFF FUTURE WORK: More exhaustive analysis using all the recordings collected during the pilot phases; Combining the results of all PERFORM classifier outputs Create a complete profile of patients WTHS_2011 | Valencia| December 2011 21
  • 22. Thank you!Matteo PastorinoUniversidad Politécnica de MadridLife Supporting Technologiesmpastorino@lst.tfo.upm.esSkype id: matteo_pasto WTHS_2011 | Valencia| December 2011 22