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Apparatus, systems and methods for predicting, screening and monitoring of encephalopathy / delirium

A technology for screening and encephalopathy, applied in medical equipment, telemetry patient monitoring, applications, etc., can solve problems such as cost, impossibility of delirium screening, and poor sensitivity

Active Publication Date: 2018-12-21
UNIV OF IOWA RES FOUND
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Screening instruments based primarily on chart reviews and patient interviews have been unsuccessful due to challenges in implementing these screening instruments into the clinical workflow and providing ongoing training for healthcare providers to use them
Additionally, they exhibit poor sensitivity in routine use
[0008] Electroencephalography (EEG) is effective in differentiating delirium from normal brain function, however, it is logistically impossible to screen for delirium because it requires skilled technicians to perform 16- to 24-lead EEG testing and paraprofessional A neurologist to interpret the study
This takes hours per patient and is nearly impossible to implement on a large number of patients in a busy hospital setting
Furthermore, EEG has not been used to predict the development of delirium, only to confirm its presence

Method used

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  • Apparatus, systems and methods for predicting, screening and monitoring of encephalopathy / delirium
  • Apparatus, systems and methods for predicting, screening and monitoring of encephalopathy / delirium
  • Apparatus, systems and methods for predicting, screening and monitoring of encephalopathy / delirium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0087] Example 1: Screening for encephalopathy by BSEEG compared to clinical diagnosis of delirium

[0088] In this embodiment, a preliminary study was carried out, using more than 80 patients aged 65 and above (both clinically diagnosed with delirium and clinically diagnosed without delirium), comparing the results obtained by screening device 10, system 1 and method 5 The patient's brain wave signal. Baseline cognitive function was assessed using the Montreal Cognitive Assessment (MoCA).

[0089] In this example, patients were subsequently screened for the presence of delirium using the Confusion Assessment Method in the Intensive Care Unit (CAM-ICU). Following the assessment, electroencephalogram (EEG) readings are obtained by BSEEG using the presently described device, system, and method, i.e., two EEG leads are placed on the patient's forehead - one for each cerebral hemisphere - to obtain a 10-minute course Two-channel signal from the right and left within. A ground w...

Embodiment 2

[0093] Example 2: Screening Device Evaluation

[0094] In this example, the initial training set of the dataset consisted of 186 total EEG samples from patients associated with clinical or CAM evidence of delirium. These samples represented 5 positive cases, 179 negative cases, and 2 negative cases excluded from further review due to insufficient data quality for analysis.

[0095] In this example, a low-pass filter of 15 Hz was initially used, but preliminary results indicated that there was an unequal attenuation in the FFT frequency information between positive and negative cases, so the low-pass filter was discarded.

[0096] During the processing of the treated samples, a window of 4 seconds was observed to be sufficient to show good results. Moreover, in this embodiment, for example as Figure 9A and 9B As shown, a threshold of 500 μV was used to exclude windows containing high-amplitude peaks.

[0097] Figure 9C and 9D The spectral density of the channels is depi...

Embodiment 3

[0103] Example 3: Machine Learning

[0104] Such as Figure 12 As shown, in some embodiments, a machine learning model (block 200) is used to identify hallmarks of delirium / encephalopathy, and can be used to modify other systems, methods, and devices described herein, such as by refining thresholds ( as combined Figure 6 -8 to 65) to improve the accuracy of diagnosis. In these embodiments, a model is used within a computing machine (block 204) to correlate individual patient data and population patient data (shown generally at block 202). In general, various machine learning methods can be coded for execution in: screening device 10, server / computing device 42, database 36, third party server 34, in operative communication with screening device 10 and / or sensor 12 other computing or electronic storage devices (also such as Figures 5A-5D shown in the implementation).

[0105] The model can be performed on data recorded from the patient 30 or otherwise (such as spectral d...

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Abstract

The disclosed apparatus, systems and methods relate to predicting, screening, and monitoring for delirium. Systems and methods may include receiving one or more signals from one or more sensing devices; processing the one or more signals to extract one or more features from the one or more signals; analyzing the one or more features to determine one or more values for each of the one or more features; comparing at least one of the one or more values or a measure based on at least one of the one or more values to a threshold; determining a presence, absence, or likelihood of the subsequent development of delirium for a patient based on the comparison; and outputting an indication of the presence, absence, or likelihood of the subsequent development of delirium for the patient.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Application No. 62 / 263,325, filed December 4, 2015, and entitled "Predicting, Screening and Monitoring of Delirium," the entire contents of which are pursuant to 35 U.S.C. § 119(e) Incorporated herein by reference. technical field [0003] The disclosed embodiments relate to systems and methods for predicting, screening, and monitoring encephalopathy / delirium, and more particularly, to methods for determining the presence, absence, or likelihood of subsequent suffering of encephalopathy / delirium in a patient through signal analysis systems and methods. Background technique [0004] Encephalopathy - commonly diagnosed and referred to as "delirium" - is a common, underdiagnosed and very dangerous medical condition. As described herein, "delirium" generally refers to a syndrome typically clinically diagnosed based on a physician's assessment of a patient's symptoms ac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/16A61B5/374
CPCA61B5/7221A61B5/7267A61B5/0006A61B5/7275A61B5/7257A61B5/726A61B2505/01G16H40/63A61B5/165A61B5/742A61B5/374A61B5/16G16H50/00
Inventor 约翰·克伦威尔篠崎元
Owner UNIV OF IOWA RES FOUND
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