Dialysis system having motor driver pressure estimation of fluid within a patient line

1 . A peritoneal dialysis (“PD”) system comprising:

a housing;

a PD fluid pump housed by the housing, the PD fluid pump including an actuator that is actuated by a motor;

a patient line fluidly coupling the PD fluid pump to a transfer set that is connected to an indwelling catheter leading into a peritoneal cavity of a patient;

a motor driver configured to control the motor and transmit an output signal that is indicative of a load on the motor;

a memory storing a machine learning algorithm that associates data related to output signals from motor drivers with known PD fluid pressures within patient lines;

a processor electrically coupled to the motor driver and the memory, the processor configured to:

transmit an input signal to activate the motor driver,

receive the output signal from the motor driver,

estimate a PD fluid pressure within the patient line by applying data from the received output signal to the machine learning algorithm, and

cause the motor driver to stop when the estimated PD fluid pressure is above a threshold value.

2 . The PD system of claim 1 , wherein the processor is further configured to:

receive a plurality of output signals from the motor driver;

sample the plurality of output signals at a specified rate using a moving window filter; and

apply the machine learning algorithm to the data from the sampled plurality of output signals that are within the moving window filter.

3 . The PD system of claim 2 , wherein the specified rate is between 50 Hz and 1000 Hz for sampling between 1 and 100 output signals within the moving window filter.

4 . The PD system of claim 2 , wherein the processor is further configured to determine at least one of the following input variable values as the data from the sampled plurality of output signals: (i) a minimum output signal value, (ii) a maximum output signal value, (iii) an average output signal value, (iv) a median output signal value, or (v) a difference of the maximum output signal value and the minimum output signal value; and

use the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter.

5 . The PD system of claim 4 , wherein the processor is further configured to:

determine or receive an indication of a flow rate of a PD fluid; and

use the flow rate of the PD fluid in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter.

6 . The PD system of claim 5 , wherein the processor is further configured to:

normalize the flow rate of the PD fluid by dividing the flow rate of the PD fluid by a maximum flow rate; and

use the normalized flow rate of the PD fluid in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter.

7 . The PD system of claim 5 , wherein the processor is configured to receive the flow rate of the PD fluid from a flow rate sensor that is fluidly coupled to the patient line or determine the flow rate of the PD fluid from a programmed PD fluid flow rate.

8 . The PD system of claim 5 , wherein the machine learning algorithm includes the following input variables as inputs:

at least one of the flow rate of the PD fluid or a normalized fluid flow rate of the PD fluid; and

at least one of (i) a minimum output signal value, (ii) a maximum output signal value, (iii) an average output signal value, (iv) a median output signal value, or (v) a difference of the maximum output signal value and the minimum output signal value.

9 . The PD system of claim 8 , wherein the machine learning algorithm additionally includes an upstream PD fluid pressure as an input variable.

10 . The PD system of claim 8 , wherein the machine learning algorithm is trained using a data set that associates at least one of the flow rate of the PD fluid or the normalized fluid flow rate of the PD fluid and the at least one of (i) to (v) with a PD fluid pressure within the patient line that is measured by a pressure sensor.

11 . The PD system of claim 4 , wherein the processor is further configured to:

receive an indication of an upstream PD fluid pressure from a pressure sensor that is located upstream from the PD fluid pump; and

use the upstream PD fluid pressure in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter.

12 . The PD system of claim 11 , wherein the upstream PD fluid pressure corresponds to a head height pressure.

13 . The PD system of claim 1 , further comprising a filter set including a hydrophilic filter membrane fluidly coupled between the patient line and the transfer set,

wherein the patient line is a dual lumen patient line including a fresh PD fluid lumen and a used PD fluid lumen, and

wherein the processor is configured to estimate the PD fluid pressure within the fresh PD fluid lumen.

14 . The PD system of claim 1 , wherein the motor is a stepper motor and the output signal represents a load angle of the stepper motor.

15 . The PD system of claim 1 , wherein the processor is further configured to apply a smoothing function to a sequence or stream of the estimated PD fluid pressures.

16 . The PD system of claim 1 , wherein the actuator includes a piston actuated by the motor.

17 . A peritoneal dialysis (“PD”) method to estimate PD fluid pressure, the method comprising:

storing in a memory of a PD machine, a machine learning algorithm that associates data related to output signals from motor drivers with known PD fluid pressures within patient lines;

transmitting from a processor of the PD machine to a motor driver of a PD fluid pump, an input signal to activate the motor driver, which causes a motor to actuate an actuator for pumping PD fluid at a specified rate from the PD fluid pump to a patient line that is fluidly coupled to a transfer set that is connected to an indwelling catheter leading into a patient's peritoneal cavity;

receiving, in the processor, output signals from the motor driver that are indicative of a load estimation output of the motor;

estimating, via the processor using the machine learning algorithm, a PD fluid pressure within the patient line based on data related to the received output signals; and

causing, via the processor, a user interface of the PD machine to display the estimated PD fluid pressure.

18 . The method of claim 17 , further comprising causing, via the processor, the motor driver to stop when the estimated PD fluid pressure is above a threshold value out outside of a specified range.

19 . The method of claim 17 , wherein the machine learning algorithm includes at least one of a Gaussian process regression, a linear regression, a logic regression, a decision tree, a gradient boosting algorithm, a random forest algorithm, a k-nearest neighbor algorithm, a k-means algorithm, a support-vector machine, a Naïve Bayes algorithm or combinations thereof.

20 . The method of claim 17 , further comprising:

sampling, via the processor, the output signals at a specified rate using a moving window filter; and

applying, via the processor, the machine learning algorithm to data from the sampled plurality of output signals that are within the moving window filter.

21 . The method of claim 20 , wherein the specified rate is between 50 Hz and 1000 Hz for sampling between 1 and 100 output signals within the moving window filter.

22 . The method of claim 20 , further comprising:

determining, via the processor, at least one of the following input variable values as the data from the sampled output signals: (i) a minimum output signal value, (ii) a maximum output signal value, (iii) an average output signal value, (iv) a median output signal value, or (v) a difference of the maximum output signal value and the minimum output signal value; and

using, via the processor, the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter.

23 . The method of claim 22 , further comprising:

determining or receiving, via the processor, an indication of a flow rate of the PD fluid; and

using, via the processor, the flow rate of the PD fluid in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter.

24 . The method of claim 22 , further comprising training the machine learning algorithm using a data set that associates at least one of a flow rate of the PD fluid or a normalized fluid flow rate of the PD fluid and the at least one of (i) to (v) with a PD fluid pressure within the patient line that is measured by a pressure sensor.