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server.R
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server.R
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function(input, output) {
data <- reactive({
validate(
need(input$fichero != "" && input$samples != "", "Please, select a file sample.")
)
ficheroCSV <- input$fichero;
#Leemos el fichero
x <- read.csv(ficheroCSV$datapath, header = TRUE);
validate(
need(length(x$volt) >= (input$samples), "El número de muestras seleccionadas es mayor que el número de muestras del fichero.")
)
#Array con las amplitudes (voltios)
x1 <- x$volt[1:as.numeric(input$samples)];
#Num de muestras
N = length(x1);
#Frecuencia de muestreo
fs = N / 10;
#Array con los tiempos
t = x$time[1:as.numeric(input$samples)];
x1 = x1 - mean(x1);
#x1 = x1 / max(abs(x1));
#Filtro de paso bajo (Low Pass Filtering)
#LPF(1 - z ^ -6) ^ 2 / (1 - z ^ -1) ^ 2
b = c(1, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 1);
a = c(1, -2, 1);
x2 = filter(b, a, x1);
x2 = x2 / max(abs(x2));
delay = 6;
# High Pass filter H(z) = (-1 + 32 z ^ (-16) + z ^ (-32)) / (1 + z ^ (-1))
b = c(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1);
a = c(1, 1);
x3 = filter(b, a, x2);
x3 = x3 / max(abs(x3));
delay = delay + 16;
#derivative filter H(z) = (1 / 8 T)(-z ^ (-2) - 2 z ^ (-1) + 2 z + z ^ (2))
b = c(1, 2, 0, -2, -1) * (1 / 8) * fs;
x4 = conv(b, x3);
x4 = x4 / max(x4);
# Squaring nonlinearly enhance the dominant peaks
x5 = x4 ^ 2;
#Movemos la ventana de integracion (Moving Window Integration)
h = matrix(1, 1, 31) / 31;
x6 = conv(x5, h);
x6 = x6[15 + (1:N)];
x6 = x6 / max(abs(x6));
#x6 = conv(x5, array(1, dim = c(1, round(0.150 * fs))) / round(0.150 * fs));
#delay = delay + round(0.150 * fs) / 2;
#plot((1:length(x6) - 1) / fs, x6)
#Busqueda del complejo QRS (Find QRS Points Which it is different than Pan - Tompkins algorithm)
#Maximo
max_h = max(x6);
#Media
thresh = mean(x6);
#TRUE cuando detecta valores que contienen el complejo QRS
poss_reg = (x6 > ((thresh * max_h) * 3));
#Array con la lista de unos (las esquinas por la izquierda)
left = which(diff(c(0, poss_reg)) %in% 1);
#Array con la lista de -1 (las esquinas por la derecha)
right = which(diff(c(poss_reg, 0)) %in% -1);
left = left - (delay);
right = right - (delay);
#Matriz con los puntos QRS
qrsValues <- matrix(0, nrow = length(left), ncol = 3)
#Matriz con los tiempos
timeValues <- matrix(0, nrow = length(left), ncol = 3)
for (i in 1:length(left)) {
#PuntoR
R_value = max(x1[left[i]:right[i]]);
indexOfR = which(x1[left[i]:right[i]] == R_value);
R_time = t[left[i] + indexOfR[1]];
R_loc = left[i] + (indexOfR[1] - 1);
#PuntoQ
Q_value = min(x1[left[i]:R_loc])[1];
indexOfQ = which(x1[left[i]:R_loc] == Q_value);
Q_time = t[left[i] + indexOfQ[1]];
Q_loc = left[i] + (Q_value - 1);
#PuntoS
S_value = min(x1[R_loc:right[i]]);
indexOfS = which(x1[left[i]:right[i]] == S_value);
if (length(indexOfS) > 1) {
S_time = t[left[i] + indexOfS[length(indexOfS)]];
}
else{
S_time = t[left[i] + indexOfS];
}
S_loc = left[i] + (S_value - 1);
#Almacenamos el momento del tiempo en el que ocurre cada evento
timeValues[i,] <- c(R_time, Q_time, S_time);
#Almacenamos los valores QRS
qrsValues[i,] <- c(R_value, Q_value, S_value);
}
###############################################################
## CÁLCULO DEL RITMO CARDÍACO
###############################################################
totalBeatsPerMinute = 0;
if (N >= 2000) {
beats = 0;
beatsPerMinute = 0;
c = 0;
for (i in 1:length(left)) {
if (i != 1 && i != length(left)) {
beats = timeValues[i + 1, 1] - timeValues[i, 1];
totalBeatsPerMinute = totalBeatsPerMinute + ((1 / beats) * 60);
c = c + 1;
}
}
totalBeatsPerMinute = totalBeatsPerMinute / c;
}
###############################################################
## CÁLCULO DE ONDAS P Y T
###############################################################
#Matriz con los puntos P
pValues <- matrix(0, nrow = length(left) + 1, ncol = 3)
#Matriz con los tiempos de P
pTimeValues <- matrix(0, nrow = length(left) + 1, ncol = 3)
#Matriz con los puntos T
tValues <- matrix(0, nrow = length(left) + 1, ncol = 3)
#Matriz con los tiempos de T
tTimeValues <- matrix(0, nrow = length(left) + 1, ncol = 3)
offset = 0;
u = 1;
for (i in 1:(length(left) + 1)) {
#Si estoy en la primera posicion
if (i == 1) {
g1 <- x1[1:left[i]];
offset = 0;
}
else {
offset = right[i - 1];
#Si estoy en la última posición
if (i == length(left) + 1) {
g1 <- x1[right[i - 1]:length(x1)];
}
else
g1 <- x1[right[i - 1]:left[i]];
}
g1 = g1 / max(abs(g1));
g1 = g1 - mean(g1);
#Filtro pasa-banda
g2 <- seewave::bwfilter(g1, fs, from = 0.5, to = 10, bandpass = TRUE, output = "matrix");
g2 <- rev(seewave::bwfilter(rev(g2), fs, from = 0.5, to = 10, bandpass = TRUE, output = "matrix"));
##########################
## CÁLCULO DE LÍMITES SEGÚN PPM
#########################
lowLimit = (totalBeatsPerMinute * 55) / 60;
highLimit = (totalBeatsPerMinute * 110) / 60;
#Paso 3: Media móvil
if (length(g2) < lowLimit)
MAPeak <- stats::filter(g2, rep(1 / lowLimit, length(g2)), method = c("convolution"), sides = 2, circular = TRUE)
else
MAPeak <- stats::filter(g2, rep(1 / lowLimit, lowLimit), method = c("convolution"), sides = 2, circular = TRUE)
if (length(g2) < highLimit)
MAPwave <- stats::filter(g2, rep(1 / highLimit, length(g2)), method = c("convolution"), sides = 2, circular = TRUE)
else
MAPwave <- stats::filter(g2, rep(1 / highLimit, highLimit), method = c("convolution"), sides = 2, circular = TRUE)
#Paso 4: Seleccionamos los bloques de interés
#Cuando la amplitud de la primera media móvil (MAPeak) es mayor que la amplitud de la segunda media móvil (MAPwave),
#se selecciona como bloque de interés
blocks <- array(-1, dim = c(1, length(MAPeak)))
for (y in 1:length(MAPeak)) {
if ((MAPeak[y] > MAPwave[y])) {
blocks[y] <- 0.25;
}
else
blocks[y] <- 0;
}
#Paso 5: Eliminamos los bloques de ruido
numBlocksGreaterThanZero = blocks > 0;
leftBlocks = which(diff(c(0, numBlocksGreaterThanZero)) %in% 1);
rightBlocks = which(diff(c(numBlocksGreaterThanZero, 0)) %in% -1);
if (length(leftBlocks) == 0 || length(rightBlocks) == 0)
break;
for (y in 1:length(leftBlocks)) {
if (length(blocks[leftBlocks[y]:rightBlocks[y]]) < (lowLimit * 0.5)) {
blocks[leftBlocks[y]:rightBlocks[y]] <- 0;
}
}
#Paso 6. Para determinar si cada bloque detectado en la sección Ri-Ri+1 contiene una onda P o T, se cuenta el número de bloques de ese intervalo.
numBlocksGreaterThanZero = blocks > 0;
leftBlocks = which(diff(c(0, numBlocksGreaterThanZero)) %in% 1);
rightBlocks = which(diff(c(numBlocksGreaterThanZero, 0)) %in% -1);
#A continuación se extrae el máximo para distinguir la onda P de la T y del ruido
#Si se ha detectado 0 bloques: error (salimos del bucle
if (length(leftBlocks) == 0 || length(rightBlocks) == 0)
break;
#Si se ha detectado 1 o más bloques:
### POSICIONES DE LAS R
#Si estoy en el primer bloque
if (i == 1) {
RTimeValue1 = 0.00;
RTimeValue2 = timeValues[i, 1];
}
#Si estoy en el último bloque (ya no hay más complejos QRS)
else if (i == length(left) + 1) {
RTimeValue1 = timeValues[i - 1, 1];
RTimeValue2 = t[length(t)];
}
#Para el resto de bloques
else {
RTimeValue1 = timeValues[i - 1, 1];
RTimeValue2 = timeValues[i, 1];
}
max = 0;
for (y in 1:length(leftBlocks)) {
## PUNTO POR LA IZQUIERDA Y POR LA DERECHA DE CADA BLOQUE DE INTERÉS
leftBlock = offset + leftBlocks[y];
rightBlock = (offset + rightBlocks[y]) - 1;
## MÁXIMO VALOR DE ESE BLOQUE DE INTERÉS
maxBlockInLoop = max(x1[leftBlock:rightBlock]);
## POSICIÓN DEL VALOR MÁXIMO DENTRO DE LA MATRIZ ORIGINAL
#Si estoy en la primera región Ri-Ri+1
if (i == 1) {
indexOfMax = (which(x1[1:rightBlock] == maxBlockInLoop));
}
#Si estoy en la última región Ri-Ri+1
else if (i == length(left) + 1) {
indexOfMax = offset + (which(x1[right[i - 1]:rightBlock] == maxBlockInLoop)) -1;
}
#Si estoy en cualquier otra región
else {
indexOfMax = offset + (which(x1[right[i - 1]:left[i]] == maxBlockInLoop));
}
if (maxBlockInLoop > max) {
if (i < length(left) + 1) {
timeValueMax = t[indexOfMax[length(indexOfMax)]];
distanceToR = RTimeValue2 - timeValueMax;
if (distanceToR > (((totalBeatsPerMinute * 0.055) / 60) * 0.5) && distanceToR < (((totalBeatsPerMinute * 0.47) / 60) * 0.5)) {
#Almacenamos los valores P
pValues[i,] <- c(maxBlockInLoop, x1[leftBlock], x1[rightBlock]);
#Almacenamos los tiempos de P
pTimeValues[i,] <- c(timeValueMax, t[leftBlock], t[rightBlock]);
}
else {
if (i == 1) {
#Calculamos la distancia
distanceBetweenActualRPeaks = RTimeValue2 - RTimeValue1;
distanceBetweenNextRPeaks = timeValues[i + 1, 1] - RTimeValue2;
distanceBetweenTwoNextRPeaks = timeValues[i + 2, 1] - timeValues[i + 1, 1];
distanceBetweenNextsRPeaks = mean(c(distanceBetweenNextRPeaks, distanceBetweenTwoNextRPeaks));
if (distanceBetweenActualRPeaks > (distanceBetweenNextsRPeaks - 0.1) && distanceBetweenActualRPeaks < (distanceBetweenNextsRPeaks + 0.1)) {
max = maxBlockInLoop;
timeValueMax = t[indexOfMax[1]];
distanceToR = timeValueMax - RTimeValue1;
## VALORES MÁXIMOS DE LAS DISTANCIAS A LAS R
RTmin = (RTimeValue2 - RTimeValue1) * ((totalBeatsPerMinute * 0.111) / 60);
RTmax = (RTimeValue2 - RTimeValue1) * ((totalBeatsPerMinute * 0.583) / 60);
if (distanceToR > RTmin && distanceToR < RTmax) {
#Almacenamos los valores T
tValues[i,] <- c(maxBlockInLoop, x1[leftBlock], x1[rightBlock]);
#Almacenamos los tiempos de P
tTimeValues[i,] <- c(timeValueMax, t[leftBlock], t[rightBlock]);
}
}
}
else {
max = maxBlockInLoop;
timeValueMax = t[indexOfMax[1]];
distanceToR = timeValueMax - RTimeValue1;
## VALORES MÁXIMOS DE LAS DISTANCIAS A LAS R
RTmin = (RTimeValue2 - RTimeValue1) * ((totalBeatsPerMinute * 0.111) / 60);
RTmax = (RTimeValue2 - RTimeValue1) * ((totalBeatsPerMinute * 0.583) / 60);
if (distanceToR > RTmin && distanceToR < RTmax) {
#Almacenamos los valores T
tValues[i,] <- c(maxBlockInLoop, x1[leftBlock], x1[rightBlock]);
#Almacenamos los tiempos de P
tTimeValues[i,] <- c(timeValueMax, t[leftBlock], t[rightBlock]);
}
}
}
}
else {
max = maxBlockInLoop;
timeValueMax = t[indexOfMax[1]];
distanceToR = timeValueMax - RTimeValue1;
## VALORES MÁXIMOS DE LAS DISTANCIAS A LAS R
RTmin = (RTimeValue2 - RTimeValue1) * ((totalBeatsPerMinute * 0.111) / 60);
RTmax = (RTimeValue2 - RTimeValue1) * ((totalBeatsPerMinute * 0.583) / 60);
if (distanceToR > RTmin && distanceToR < RTmax) {
#Almacenamos los valores T
tValues[i,] <- c(maxBlockInLoop, x1[leftBlock], x1[rightBlock]);
#Almacenamos los tiempos de P
tTimeValues[i,] <- c(timeValueMax, t[leftBlock], t[rightBlock]);
}
else {
#Calculamos la distancia
distanceBetweenActualRPeaks = RTimeValue2 - RTimeValue1;
distanceBetweenPreviousRPeaks = RTimeValue1 - timeValues[i - 2, 1];
distanceBetweenTwoPreviousRPeaks = timeValues[i - 2, 1] - timeValues[i - 3, 1];
distanceBetweenPreviousRPeaks = mean(c(distanceBetweenPreviousRPeaks, distanceBetweenTwoPreviousRPeaks));
if (distanceBetweenActualRPeaks > (distanceBetweenPreviousRPeaks - 0.1) && distanceBetweenActualRPeaks < (distanceBetweenPreviousRPeaks + 0.1)) {
timeValueMax = t[indexOfMax[length(indexOfMax)]];
distanceToR = RTimeValue2 - timeValueMax;
if ((distanceToR > (((totalBeatsPerMinute * 0.055) / 60) * 0.5)) && (distanceToR < (((totalBeatsPerMinute * 0.47) / 60) * 0.5))) {
#Almacenamos los valores P
pValues[i,] <- c(maxBlockInLoop, x1[leftBlock], x1[rightBlock]);
#Almacenamos los tiempos de P
pTimeValues[i,] <- c(timeValueMax, t[leftBlock], t[rightBlock]);
}
}
}
}
}
else {
#Solo analizo si no estoy en la último posición. Si estoy en la última posición es que ya no hay más complejos QRS, por lo que no puedo detectar la onda P con seguridad
if (i < length(left) + 1) {
timeValueMax = t[indexOfMax[length(indexOfMax)]];
distanceToR = RTimeValue2 - timeValueMax;
if ((distanceToR > (((totalBeatsPerMinute * 0.055) / 60) * 0.5)) && (distanceToR < (((totalBeatsPerMinute * 0.47) / 60) * 0.5))) {
#Almacenamos los valores P
pValues[i,] <- c(maxBlockInLoop, x1[leftBlock], x1[rightBlock]);
#Almacenamos los tiempos de P
pTimeValues[i,] <- c(timeValueMax, t[leftBlock], t[rightBlock]);
}
}
}
}
}
#Datos a retornar
data <- list(x1 = x1, t = t, qrsColors = c("green", "blue", "red"), left = left, qrsTimeValues = timeValues, qrsValues = qrsValues,
pValues = pValues,
pTimeValues = pTimeValues,
tValues = tValues,
tTimeValues = tTimeValues,
ptColors = c("purple", "orange"),
totalBeatsPerMinute = totalBeatsPerMinute
);
})
output$plot1 <- renderPlot({
options(encoding = "UTF-8")
options(repos = c(CRAN = "https://cran.rstudio.com/"));
library(signal, warn.conflicts = FALSE);
library(seewave);
data <- data()
if (is.null(data))
return(NULL);
plot(data$t, data$x1, type = "l", xlab = "time", ylab = "volts", ylim = c(-1, 1.4))
legend(0, 1.4, c("P", "Q", "R", "S", "T"), pch = c(15, 15, 15, 15, 15), lwd = c(2.5, 2.5, 2.5, 2.5, 2.5), col = c("orange", "blue", "green", "red", "purple"))
#legend(0, 1.2, c("P", "T"), lty = c(1, 1), lwd = c(2.5, 2.5), col = c("orange", "purple"))
for (i in 1:length(data$left)) {
for (x in 1:3) {
points(data$qrsTimeValues[i, x], data$qrsValues[i, x], pch = 15, col = data$qrsColors[x]);
}
}
for (i in 1:(length(data$left)+1)) {
for (x in 1:3) {
if (data$tTimeValues[i, x] != 0 && data$tValues[i, x] != 0)
points(data$tTimeValues[i, x], data$tValues[i, x], pch = 15, col = data$ptColors[1]);
if (data$pTimeValues[i, x] != 0 && data$pValues[i, x] != 0)
points(data$pTimeValues[i, x], data$pValues[i, x], pch = 15, col = data$ptColors[2]);
}
}
if (data$totalBeatsPerMinute > 0) {
#text(x = 0.5, y = 1.4, labels = paste("Ppm:", round(data$totalBeatsPerMinute, digits = 2), sep = " "));
}
})
}