Application and device for diagnosing Alzheimer's disease
A technology for Alzheimer's disease, diagnoser, applied in the directions of diagnosis, application, diagnosis recording/measurement, etc., can solve the problems of boring process, long test time, and difficult for AD patients to complete it.
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Embodiment 1
[0241] Embodiment 1 establishes diagnostic model A
[0242] according to figure 2 , 4 The method or device establishes the diagnosis model A and performs the diagnosis.
[0243] Randomly select 15 participants with Alzheimer's disease (MMSE scale score 15-20 points, 8 males, 7 females) and 15 healthy participants (MMSE scale score above 26 points, males 8 people, 7 women) as a training set, record each person's education level (1 means elementary school, 2 means junior high school, 3 means high school) and actual age.
[0244] (1) pitch test
[0245] According to the actual physical and mental state of the 30 participants, choose Angloon, thumb piano (Kalimba kalimba) or piano instrument respectively. Before the test, show the selected musical instruments to the participants, and perform a demonstration of playing the tones, and ask the participants to try until they can play the tones proficiently.
[0246] Intonation test 1: The staff will play the three tones called...
Embodiment 2
[0283] Embodiment 2 establishes diagnostic model B
[0284]According to the education level of all people in embodiment 1, intonation test 1 (YZ1), rhythm test 1 (DJ1) data and MMSE diagnosis result, carry out parameter training by logistic regression algorithm, obtain diagnosis model B as follows:
[0285] Y=27.663+(-2.180)×R 1 +(-1.989)×R 2 +(-2.701)×R 5
[0286] Among them, Y represents the diagnosis result, R 1 Indicates education level, R 2 Indicates pitch test 1 score, R 5 Indicates the score of rhythm test 1; and the threshold value is 0, that is, Y>0, the diagnosis result is diseased, otherwise the diagnosis result is not diseased.
Embodiment 3
[0287] Embodiment 3 establishes diagnostic model C
[0288] According to the education level of all people in embodiment 1, pitch test 1 (YZ1), pitch test 2 (YZ2), rhythm test 1 (DJ1), rhythm test 2 (DJ2) data and MMSE diagnosis result, carry out by logistic regression algorithm Parameter training, the diagnosis model C is obtained as follows:
[0289] Y=15.4818+0.8629×R 1 +(-0.7602)×R 2 +(-0.5532)×R 3 +0.3974×R 5 +(-2.9928)×R 6
[0290] Among them, Y represents the diagnosis result, R 1 Indicates education level, R 2 Indicates pitch test 1 score, R 3 Indicates pitch test 2 score, R 5 Indicates the rhythm test 1 score, R 6 Indicates the score of rhythm test 2; and the threshold value is 0.667, that is, Y>0.667, the diagnosis result is diseased, otherwise the diagnosis result is not diseased.
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