Model Validation accuracy stuck at 0.65671 Keras
I would like to see you data set :)
I am also doing some signal classification.

Unless there is some simple bug in data preprocessing stage: (check what you didn't show here first!)


As correctly pointed to you by @stefanLopez your number of filters is way too low.
Next, filter length is too short to capture anything serious.
Remove batchnorm while testing.
Reduce dropout while testing.
Test with ELU (Exponential Linear Unit) activation.
Last, use more FC layers with more neurons.
Try using glorot (commonly known as Xavier) initializer.


Example model:

model=Sequential()

model.add(Conv1D(filters=24,kernel_size=16,strides=1,padding='valid',activation='elu',kernel_initializer='glorot_normal',input_shape=X_train.shape[1::]))

model.add(Conv1D(filters=16,kernel_size=9,strides=1,padding='same',activation='elu',kernel_initializer='glorot_normal'))
model.add(Dropout(0.1))

model.add(Conv1D(filters=12,kernel_size=9,strides=1,padding='valid',activation='elu',kernel_initializer='glorot_normal'))
model.add(Dropout(0.1))

model.add(Flatten())
model.add(Dense(128,activation='elu'))
model.add(Dropout(0.1))
model.add(Dense(16,activation='elu'))
model.add(Dropout(0.1))
model.add(Dense(1,activation='sigmoid'))


Tell if it helps.