Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

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Non-invasive blood pressure estimation using phonocardiogram

2017 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2017.8050240 ◽

2017 ◽

Cited By ~ 6

Author(s):

Amirhossein Esmaili Dastjerdi ◽

Mohammad Kachuee ◽

Mahdi Shabany

Keyword(s):

Blood Pressure ◽

Invasive Blood Pressure ◽

Non Invasive ◽

Pressure Estimation ◽

Blood Pressure Estimation

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Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

Laser Physics Letters ◽

10.1088/1612-202x/aa7c48 ◽

2017 ◽

Vol 14 (9) ◽

pp. 095601 ◽

Cited By ~ 3

Author(s):

Huimin Sun ◽

Yaoyong Meng ◽

Pingli Zhang ◽

Yajing Li ◽

Nan Li ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Principal Component ◽

Non Invasive ◽

Artificial Neural

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Abstract P365: Blood Pressure Evaluation From Ppg Signal Analysis and Artificial Neural Network

Hypertension ◽

10.1161/hyp.70.suppl_1.p365 ◽

2017 ◽

Vol 70 (suppl_1) ◽

Author(s):

Francesco Lamonaca ◽

Vitaliano Spagnuolo ◽

Serena De Prisco ◽

Domenico L Carnì ◽

Domenico Grimaldi

Keyword(s):

Neural Network ◽

Blood Pressure ◽

Artificial Neural Network ◽

Audio Signal ◽

Hardware Interface ◽

Validation Procedure ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Time Domain ◽

And Storage

The analysis of the PPG signal in the time domain for the evaluation of the blood pressure (BP) is proposed. Some features extracted from the PPG signal are used to train an Artificial Neural Network (ANN) to determine the function that fit the target systolic and diastolic BP. The data related to the PPG signals and BP used in the analysis are provided by the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC II) database. The pre-analysis of the signal to remove inconsistent data is also proposed. A set of 1750 valid pulse is considered. The 80% of the input samples is used for the training of the network. Instead, the 10% of the input data are used for the validation of the network and 10% for final test of this last. The results show as the error for both the systolic and diastolic BP evaluation is included in the range of ±3 mmHg. Tab.1 shows the results for 20 PPG pulses randomly selected analyzed together with the systolic and diastolic blood pressure furnished by MIMC and evaluated by the trained ANN. Tab.1 experimental results comparing MIMIC and the ANN results. Moreover, a suitable hardware to validate the ANN with the sphygmomanometer is designed and realized. This hardware allows clinicians to collect data according to the requirements of the validation procedure. With the sphygmomanometer the systolic and diastolic values are referred to two different PPG pulses. As a consequence, it is proposed a new hardware interface allowing the synchronized acquisition and storage of the PPG signal and clinician voice. For the validation, the clinician: (i) evaluates the BP on both the arms and assesses that no significant differences occur; (ii) plugs the PPG sensor on the finger of one arm; (iii) starts the recording of both the PPG signal and the audio signal; (iv) evaluates the BP on the other arm with sphygmomanometer and says the systolic and diastolic values when detected. Through suitable post processing algorithm, the Systolic and Diastolic values are associated to the corresponding PPG Pulses. Following this procedure, the dataset to further validate the ANN according the standard is obtained. Once the ANN is validated it will be implemented on smartphone to have always in the pocket a reliable measurement system for Blood Pressure, oximetry and heart rate.

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